• Vehicle detection using yolo darknet

    2 Окт 2012 Инна 3

    vehicle detection using yolo darknet

    YOLO: Vehicle Detection for Self-Driving. Did You Notice How Natural Simba's Movements Looked In The Movie? How They Made Little Simba Look So Lifelike in. Human & Object Detection and Tracking on Recorded Videos with YOLO in Matlab Завершено Car and plate detection using image processing Завершено left. Keywords traffic signs detection, deep convolutional neural network, YOLO v3, fully autonomous vehicles is their vision that makes applied in real time.

    Vehicle detection using yolo darknet

    Certain algorithms are focused only on models. The second signs location on the image without attention. Therefore, remains an open issue. Architecture of the system is shown algorithm of version 3 [10—12] aimed at architecture of in Fig. Recently, deep CNNs were not is fed to the model However, modern GPUs preprocessed in the same way as it was done for training graphics processing units were especially developed to normalization and subtraction of mean image.

    The result implement high performance. Every single line describes one bounding box. The last category, in the paper [15]. Resulted dataset was divided into sub-datasets amount of RGB images. The total amount of images for training and , and images, respectively. Before validation is and Consequently, coordinates were converted accuracy [15]. To height. Calculations were made by the prohibitory, danger, mandatory and others. Then, the mean following equations: of these calculated APs across all classes produces mAP.

    AP, in turn, is calculated by considering an area under interpolated Precision axis y and Recall axis x curve [16, 17]. The curve represents performance of the trained model-1 by plotting a zig-zag graph of Precisions values against Recalls values. Firstly, 11 points are located on Recall curve as following: 0; 0.

    Then, the average of maximum Precision values is computed for these 11 Recall points. Precision illustrates how accurate predicted bounding boxes are and demonstrates an ability of the model-1 to detect relevant objects. Recall illustrates all correct where Xmin, Ymin, Xmax and Ymax are original coordinates; predictions of bounding boxes among all relevant ground w and h are real image width and real image height truth bounding boxes and demonstrates an ability of the respectively.

    Next, Precision and Recall are calculated number of ground truth bounding boxes that is As can be seen from Table 3, the total number of detections at iterations is It means, that there are 3 and 4 bounding boxes with wrong predictions FP for where TP True Positive represents the number of the classes, mandatory and other, respectively.

    Nine ground bounding boxes with correct predictions; FP False truth bounding boxes among were not detected FN. If IoU is in the range 0; threshold , Memory. Consequently, mAP for the entire model-1 can be in the range from 6 to 1 respectively. These platforms have Model-1 used Darknet framework to be trained in. RAM in the range of 4—32 Gb and can process minimum Parameters used for the training are described in Table 2. Before feeding to the network, the for the proposed method after training.

    Due to a number of categories Images were also collected in the batches with 64 and small amount of images in the dataset for training, items each. Sixteen was set as a number of subdivisions. As for detection, were processed during one iteration. Weights were updated after each such iteration. To predict bounding boxes, anchors priors were used Table 2. Parameters for the model-1 to be trained with at each scale. The anchors were calculated by k-means clustering for COCO dataset. The last three anchors, scale 1 large object , 90 , , , parameters in Table 2 randomly changed saturation, , exposure and hue during training.

    It also shows the highest exposure 1. The images for validation have unique hue 0. Loss and mAP graph during training of model-1 4 categories. In [1], the authors used among one of the 43 classes. In [2], the mAP 0. The authors in [2] also used categories. Table 3. Kolyubin Fig. References Литература 1. Zhu Y. Neurocomputing, , vol. Tabernik D. Chung J. Neural Processing Letters, , in press. Mehta S. Ren S. Faster R-CNN: towards real-time 5. After removing the fully connected layers, YOLO can take images of different sizes.

    If the width and height are doubled, we are just making 4x output grid cells and therefore 4x predictions. Since the YOLO network downsamples the input by 32, we just need to make sure the width and height is a multiple of For every 10 batches, YOLOv2 randomly selects another image size to train the model.

    This acts as data augmentation and forces the network to predict well for different input image dimension and scale. In additional, we can use lower resolution images for object detection at the cost of accuracy. This can be a good tradeoff for speed on low GPU power devices.

    At high-resolution YOLO achieves VGG16 requires We can further simplify the backbone CNN used. Darknet requires 5. It also uses global average pooling to make predictions. Here is the detail network description:. YOLO is trained with the ImageNet class classification dataset in epochs: using stochastic gradient descent with a starting learning rate of 0. After the training, the classifier achieves a top-1 accuracy of Then the fully connected layers and the last convolution layer is removed for a detector.

    YOLO trains the network for epochs with a starting learning rate of 10 -3 , dividing it by 10 at 60 and 90 epochs. YOLO uses a weight decay of 0. Datasets for object detection have far fewer class categories than those for classification.

    It trains the end-to-end network with the object detection samples while backpropagates the classification loss from the classification samples to train the classifier path. The children form an is-a relationship with its parent like biplane is a plane. But the merged labels are now not mutually exclusive. Instead of predicting labels in a flat structure, we create the corresponding WordTree which has leave nodes for the original labels and nodes for their parent classes.

    Originally, YOLO predicts the class score for the biplane. But with the WordTree, it now predicts the score for the biplane given it is an airplane. One benefit of the hierarchy classification is that when YOLO cannot distinguish the type of airplane, it gives a high score to the airplane instead of forcing it into one of the sub-categories. When YOLO sees a classification image, it only backpropagates classification loss to train the classifier.

    YOLO finds the bounding box that predicts the highest probability for that class and it computes the classification loss as well as those from the parents. If an object is labeled as a biplane, it is also considered to be labeled as airplane, air, vehicle… This encourages the model to extract features common to them.

    So even we have never trained a specific class of objects for object detection, we can still make such predictions by generalizing predictions from related objects. In object detection, we set Pr physical object equals to the box confidence score which measures whether the box has an object. YOLO traverses down the tree, taking the highest confidence path at every split until it reaches some threshold and YOLO predicts that object class.

    During the evaluation, YOLO test images on categories that it knows how to classify but not trained directly to locate them, i. It shares about 44 categories with COCO. Therefore, the dataset contains categories that have never been trained directly on how to locate them. YOLO extracts similar features for related object types. Hence, we can detect those categories by simply from the feature values.

    YOLO gets YOLO performs well with new species of animals not found in COCO because their shapes can be generalized easily from their parent classes. An autonomous car also known as a driverless car and a self-driving car is a vehicle that is capable of sensing its environment and navigating without human input. Солнцезащитные очки, выпуклая линза, которая собирает солнечный свет для воспламенения. В GWT из-за ограничения механизма сохранности Serialize сервер выдает исключение.

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    Мы не отыскали пригодной мини-программы. Вы сможете проверить эти 5 мини-программ, которыми по Чтоб применять jbpm для реализации обработки рабочего процесса, обработка прав доступа неизбежна. Российские Блоги. Основная Свяжитесь с нами. Accuracy improvements Batch normalization Add batch normalization in convolution layers. More diverse predictions For example, we can create 5 anchor boxes with the following shapes.

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    Vehicle detection using yolo darknet татуировки конопли для девушки vehicle detection using yolo darknet

    ТОР БРАУЗЕР КАК НАСТРОИТЬ РУССКИЙ ЯЗЫК HIDRA

    YOLOv3 uses a few tricks to improve training and increase performance, including: multi-scale predictions, a better backbone classifier, and more. The full details are in our paper! This post will guide you through detecting objects with the YOLO system using a pre-trained model.

    Or instead of reading all that just run:. You will have to download the pre-trained weight file here MB. Or just run this:. Darknet prints out the objects it detected, its confidence, and how long it took to find them. Instead, it saves them in predictions.

    You can open it to see the detected objects. Since we are using Darknet on the CPU it takes around seconds per image. If we use the GPU version it would be much faster. The detect command is shorthand for a more general version of the command.

    It is equivalent to the command:. Instead of supplying an image on the command line, you can leave it blank to try multiple images in a row. Instead you will see a prompt when the config and weights are done loading:. Once it is done it will prompt you for more paths to try different images. Use Ctrl-C to exit the program once you are done. By default, YOLO only displays objects detected with a confidence of. For example, to display all detection you can set the threshold to We have a very small model as well for constrained environments, yolov3-tiny.

    To use this model, first download the weights:. Then run the command:. You can train YOLO from scratch if you want to play with different training regimes, hyper-parameters, or datasets. You can find links to the data here. To get all the data, make a directory to store it all and from that directory run:. Now we need to generate the label files that Darknet uses. Darknet wants a. After a few minutes, this script will generate all of the requisite files. The pixel value of the adjacent continuous road surface areas is close to the seed point pixel value.

    Finally, the hole filling and morphological expansion operations are performed to more completely extract the road surface. We extracted the road surfaces of different highway scenes Fig. Process of road surface area extraction. Road surface extraction results for different highway scenarios.

    We segmented the road surface area to provide accurate input for subsequent vehicle detection. For the extracted road surface image, a minimum circumscribed rectangle is generated for the image without rotation. The near proximal area and the near remote area overlap by pixels as shown in the red part of Fig. The pixel values of the near proximal area and the near remote area are searched column by column. If the pixel values in the column are all zero, the image of the column is all black and is not the road surface area; it is then deleted.

    After the not-road-surface areas are excluded, the reserved areas are called remote areas and proximal areas of the road surface. This section describes the object detection methods used in this study. The implementation of the highway vehicle detection framework used the YOLOv3 network. The convolutional neural network is used to extract the features of the input image.

    The centre of the object label box is in a grid unit, and the grid unit is responsible for predicting the object. This structure adopts the full convolution method and replaces the previous version of the direct-connected convolutional neural network with the residual structure. The branch is used to directly connect the input to the deep layer of the network. Direct learning of residuals ensures the integrity of image feature information, simplifies the complexity of training, and improves the overall detection accuracy of the network.

    In YOLOv3, each grid unit will have three bounding boxes of different scales for one object. The candidate box that has the largest overlapping area with the annotated box will be the final prediction result. Additionally, the YOLOv3 network has three output scales, and the three scale branches are eventually merged. Shallow features are used to detect small objects, and deep features are used to detect large objects; the network can thus detect objects with scale changes.

    The detection speed is fast, and the detection accuracy is high. Traffic scenes taken by highway surveillance video have good adaptability to the YOLOv3 network. The network will finally output the coordinates, confidence, and category of the object. Since the image is segmented, the size of the remote road surface becomes deformed and larger.

    Therefore, more feature points of a small vehicle object can be acquired to avoid the loss of some object features due to the vehicle object being too small. The vehicle object detection model can detect three types of vehicles: cars, buses, and trucks Fig. Because there are few motorcycles on the highway, they were not included in our detection.

    The remote area and proximal area of the road surface are sent to the network for detection. The detected vehicle box positions of the two areas are mapped back to the original image, and the correct object position is obtained in the original image. Using the vehicle object detection method for obtaining the category and location of the vehicle can provide necessary data for object tracking. The above information is sufficient for vehicle counting, and the vehicle detection method thus does not detect the specific characteristics of the vehicle or the condition of the vehicle.

    Segmented image sent to the detection network and detected results merging. In this study, the ORB algorithm was used to extract the features of the detected vehicles, and good results were obtained. The ORB algorithm shows superior performance in terms of computational performance and matching costs. The coordinate system is established by taking the feature point as the centre of the circle and using the centroid of the point region as the x-axis of the coordinate system.

    Therefore, when the image is rotated, the coordinate system can be rotated according to the rotation of the image, and the feature point descriptor thus has rotation consistency. When the picture angle is changed, a consistent point can also be proposed. After obtaining the binary feature point descriptor, the XOR operation is used to match the feature points, which improves the matching efficiency.

    The tracking process is shown in Fig. When the number of matching points obtained is greater than the set threshold, the point is considered to be successfully matched and the matching box of the object is drawn. The source of the prediction box is as follows: feature point purification is performed using the RANSAC algorithm, which can exclude the incorrect noise points of the matching errors, and the homography matrix is estimated.

    According to the estimated homography matrix and the position of the original object detection box, a perspective transformation is performed to obtain a corresponding prediction box. We used the ORB algorithm to extract feature points in the object detection box obtained by the vehicle detection algorithm.

    The object feature extraction is not performed from the entire road surface area, which dramatically reduces the amount of calculation. In object tracking, the prediction box of the object in the next frame is drawn since the change of the vehicle object in the continuous frame of the video is subtle according to the ORB feature extracted in the object box.

    If the prediction box and the detection box of the next frame meet the shortest distance requirement of the centre point, the same object successfully matches between the two frames Fig. We define a threshold T that refers to the maximum pixel distance of the detected centre point of the vehicle object box, which moves between two adjacent video frames. The positional movement of the same vehicle in the adjacent two frames is less than the threshold T.

    Therefore, when the centre point of the vehicle object box moves over T in the two adjacent frames, the cars in the two frames are not the same, and the data association fails. Considering the scale change during the movement of the vehicle, the value of the threshold T is related to the size of the vehicle object box. Different vehicle object boxes have different thresholds. This definition can meet the needs of vehicle movement and different input video sizes. T is calculated by Eq. We delete the trajectory that is not updated for ten consecutive frames, which is suitable for the camera scene with a wide-angle of image collection on the highway under study.

    In this type of scene, the road surface captured by the camera is distant. In ten consecutive video frames, the vehicle will move farther away. Therefore, when the trajectory is not updated for ten frames, the trajectory is deleted. At the same time, the vehicle trajectory and the detection line will only intersect once, and the threshold setting thus does not affect the final counting result.

    If the prediction box fails to match in consecutive frames, the object is considered to be absent from the video scene, and the prediction box is deleted. From the above process, the global object detection results and tracking trajectories from the complete highway monitoring video perspective are obtained.

    This section describes the analysis of the trajectories of moving objects and the counting of multiple-object traffic information. Most of the highways are driven in two directions, and the roads are separated by isolation barriers.

    According to the direction of the vehicle tracking trajectory, we distinguish the direction of the vehicle in the world coordinate system and mark it as going to the camera direction A and driving away from the camera direction B. A straight line is placed in the traffic scene image as a detection line for vehicle classification statistics. The road traffic flow in both directions is simultaneously counted. When the trajectory of the object intersects the detection line, the information of the object is recorded.

    Finally, the number of objects in different directions and different categories in a certain period can be obtained. Our experiment used high definition highway videos for three different scenes, as shown in Fig. We used the YOLOv3 network for vehicle object detection and our established dataset for network training. In network training, there is no perfect solution for the dataset division. Our dataset dividing method follows the usual usage.

    Our dataset has 11, images, the training set images, and the test set images are randomly selected from the dataset. Due to a large number of dataset pictures, the rate of the test set and training set is sufficient to obtain the model. To obtain an accurate model, the rate of the training set should be high. The training set has 8, images, and numerous vehicle samples can be trained to obtain accurate models for detecting cars, buses, and truck targets.

    The test set has images with vehicle targets that are completely different from the training set, which is sufficient to test the accuracy of the model that has been trained. We used a batch size of 32 and set the weight attenuation to 0. We used a learning rate of 0. This approach made the gradient fall reasonably and made the loss value lower.

    To improve the detection effect of small objects, we did not discard samples with less than 1-pixel value during training but put them into the network for training. We output the result of splicing the feature map of the previous layer of the routing layer before the last yolo layer of Darknet and the 11th layer of Darknet We set the step size to 4 in the upsampling layer before the last yolo layer. After the input resolution is increased, when the network is output in the yolo layer, it can have a correspondingly larger resolution and can thus improve the accuracy of the object detection.

    A continuous frames of images were used for vehicle detection in a variety of highway scenes by using our trained model. We extracted and divided the road surface area and put it into the network for vehicle detection. We compared the number of object detections under different methods with the actual number of vehicles, as shown in Table 3. Single-frame video object detection results. Compared with the actual number of vehicles, our method comes close to the actual number of vehicles when the proximal area object of the road is large.

    The full-image detection method did not detect a large number of small objects in the remote area of the road. Our method effectively improves the detection of small objects in the remote area of the road. At the same time, in the proximal area of the road, our method is also better than the full-image detection method.

    However, the deviation is inaccurate. CNN may detect the wrong object or detect the non-object as an object, which results in an inaccurate total number of vehicles. Therefore, we calculated the average accuracy of the dataset in Table 4. We used a set of thresholds [0, 0. For recall greater than each threshold the threshold in the experiment is 0. The above 11 precisions are calculated, and ap is the average of these 11 p max recall.

    We used this value to describe the quality of our model. We obtained a final map value of It can be concluded from the above analysis that the correct overall rate of our object detection is After obtaining the object box, we performed vehicle tracking based on the ORB feature point matching method and performed trajectory analysis. In the experiment, when the matching point of each object was greater than ten, the corresponding ORB prediction position was generated.

    Based on the direction in which the tracking trajectory was generated, we used the detection line to judge the direction of motion of the vehicle and classify it for counting. We used the real time rate to evaluate the speed of the system proposed in this paper, which is defined as the ratio of the time required for the system to process a video to that of the original video played. In Eq. The smaller the real time rate value is, the faster the system performs the calculations.

    When the value of the real time rate is less than or equal to 1, the input video can be processed in real time. The results are shown in Table 5. The results show that the average accuracies of vehicle driving direction and vehicle counting are In the highway monitoring video, the car class has a small object and is easily blocked by large vehicles. At the same time, there will be multiple cars in parallel, which will affect the accuracy of the track counting. Our original video runs at 30 frames per second.

    From the calculation of the speed, it can be found that the vehicle tracking algorithm based on the ORB feature is fast. The system processing speed is related to the number of vehicles in the scene. The greater the number of vehicles, the more features need to be extracted, and the system processing time will thus become longer. In general, the vehicle counting system proposed in this manuscript is very close to real-time processing. This study established a high-definition vehicle object dataset from the perspective of surveillance cameras and proposed an object detection and tracking method for highway surveillance video scenes.

    A more effective ROI area was obtained by the extraction of the road surface area of the highway. The YOLOv3 object detection algorithm obtained the end-to-end highway vehicle detection model based on the annotated highway vehicle object dataset.

    To address the problem of the small object detection and the multi-scale variation of the object, the road surface area was defined as a remote area and a proximal area. The two road areas of each frame were sequentially detected to obtain good vehicle detection results in the monitoring field.

    The position of the object in the image was predicted by the ORB feature extraction algorithm based on the object detection result. Then, the vehicle trajectory could be obtained by tracking the ORB features of multiple objects. Finally, the vehicle trajectories were analyzed to collect the data under the current highway traffic scene, such as driving direction, vehicle type, and vehicle number.

    The experimental results verified that the proposed vehicle detection and tracking method for highway surveillance video scenes has good performance and practicability. Compared with the traditional method of monitoring vehicle traffic by hardware, the method of this paper is low in cost and high in stability and does not require large-scale construction or installation work on existing monitoring equipment. According to the research reported in this paper, the surveillance camera can be further calibrated to obtain the internal and external parameters of the camera.

    The position information of the vehicle trajectory is thereby converted from the image coordinate system to the world coordinate system. The vehicle speed can be calculated based on the calibration result of the camera. Combined with the presented vehicle detection and tracking methods, abnormal parking events and traffic jam events can be detected to obtain more abundant traffic information. In summary, vehicles in Europe, such as in Germany, France, the United Kingdom, and the Netherlands, have similar characteristics to the vehicles in our vehicle dataset, and the angle and height of the road surveillance cameras installed in these countries can also clearly capture the long-distance road surface.

    Therefore, the methodology and results of the vehicle detection and counting system provided in this analysis will become important references for European transport studies. Other datasets analysed during the current study are not publicly available due privacy reasons.

    The datasets contain personal data that may not be publicly available. It was assured that data generated for the research project are only used for this research context. Al-Smadi, M. Traffic surveillance: A review of vision based vehicle detection, recognition and tracking. International Journal of Applied Engineering Research , 11 1 , — Google Scholar. Radhakrishnan, M. Video object extraction by using background subtraction techniques for sports applications.

    Digital Image Processing , 5 9 , 91— Qiu-Lin, L. Vehicles detection based on three-frame-difference method and cross-entropy threshold method. Computer Engineering , 37 4 , — Liu, Y. Optical flow based urban road vehicle tracking. Park, K. Video-based detection of street-parking violation. In International Conference on Image Processing. Ferryman, J.

    A generic deformable model for vehicle recognition. British Machine Vision Association. Han, D. Vehicle class recognition from video-based on 3d curve probes. Zhao, Z. Object detection with deep learning: A review. Girshick, R. Rich feature hierarchies for accurate object detection and semantic segmentation.

    Uijlings, J. Selective search for object recognition. International Journal of Computer Vision , 2 , — Article Google Scholar. Kaiming, H. Spatial pyramid pooling in deep convolutional networks for visual recognition. Liu, W. Ssd: Single shot multibox detector. In European conference on computer vision. Springer International Publishing, pp. Chapter Google Scholar. Redmon, J. You only look once: Unified, real-time object detection. In IEEE conference on computer vision and pattern recognition.

    IEEE, pp. Erhan, D. Scalable object detection using deep neural networks. Yolov3: An incremental improvement. Cai, Z. A unified multi-scale deep convolutional neural network for fast object detection. Hu, X.

    Sinet: A scale-insensitive convolutional neural network for fast vehicle detection. Palubinskas, G. Model based traffic congestion detection in optical remote sensing imagery. European Transport Research Review , 2 2 , 85— Nielsen, A. The regularized iteratively reweighted mad method for change detection in multi-and hyperspectral data. Rosenbaum, D. Towards automatic near real-time traffic monitoring with an airborne wide angle camera system. European Transport Research Review , 1 1 , 11— Canny, J.

    A computational approach to edge detection. Asaidi, H. Shadow elimination and vehicles classification approaches in traffic video surveillance context. Negri, P. A cascade of boosted generative and discriminative classifiers for vehicle detection.

    Fan, Q. A closer look at faster r-cnn for vehicle detection. Luo, W. Multiple object tracking: A literature review. Xing, J. Multi-object tracking through occlusions by local tracklets filtering and global tracklets association with detection responses. Zhou, H. Object tracking using sift features and mean shift. Rublee, E. Orb: an efficient alternative to sift or surf. In International Conference on Computer Vision. Luo, Z. Traffic analysis of low and ultra-low frame-rate videos, Doctoral dissertation.

    Geiger, A. Are we ready for autonomous driving? Zhe, Z. Traffic-sign detection and classification in the wild. Krause, J.

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