Query: yolo vehicle detection

title: UAV YOLO: Small Object Detection on Unmanned Aerial Vehicle Perspective
abstract: Object detection, as a fundamental task in computer vision, has been developed enormously, but is still challenging work, especially for Unmanned Aerial Vehicle (UAV) perspective due to small scale of the target. In this study, the authors develop a special detection method for small objects in UAV perspective. Based on YOLOv3, the Resblock in darknet is first optimized by concatenating two ResNet units that have the same width and height. Then, the entire darknet structure is improved by increasing convolution operation at an early layer to enrich spatial information. Both these two optimizations can enlarge the receptive filed. Furthermore, UAV viewed dataset is collected to UAV perspective or small object detection. An optimized training method is also proposed based on collected UAV viewed dataset. The experimental results on public dataset and our collected UAV viewed dataset show distinct performance improvement on small object detection with keeping the same level performance on normal dataset, which means our proposed method adapts to different kinds of conditions.

title: YOLO Vehicle Detector Based on BN Scaling Factor Pruning
abstract: Deep learning makes object detection easy, but the network structure is complex and there is a lot of redundancy, which results in a very slow operation. This paper proposes a pruning method based on the BN scaling factor to prune the redundant structure of the network and apply it to vehicle detection tasks. First, train the network for channel sparsity to make it easier to screen out important channels. While training network weights, the L1 norm is applied to the scale factor and bias of BN. Structures with smaller BN layer parameter values are penalized. Secondly, the network is automatically pruned to obtain the minimum structure. The BN scaling factors in the network structure are sorted, and the structures with smaller scaling factors are pruned according to the proportion. Then, the bias of the pruned structure is transferred to subsequent layers to maintain accuracy. In this paper, the pruning strategy considers all convolutional layers, so a high pruning rate is obtained, and the model after pruning can still maintain high accuracy without any training. For vehicle data, after training and testing, the accuracy of the model after pruning only decreased from 50.4Map to 47.27Map, but the network structure reached 76% compression. GFLOPS decreased from 33.12 to 8.16, and the number of million parameters decreased from 61.95 to 6.95.

title: Simultaneous Vehicle Detection and Classification Model based on Deep YOLO Networks
abstract: Due to the rapid growth of vehicles, traffic monitoring and tracking systems in the last decade, vehicle detection and extracting information such as vehicle type and model and car plate recognition are the important issues of the day and many efforts have been made using different methods to identify vehicles. Given the high number of vehicles and similarities of classes, it is a difficult task to find an accurate and rapid approach to differentiate available classes and classify them. In this article we propose a new approach which simultaneously detects vehicles and identifies their type. Two models are trained in this paper. The first model is based on CNN networks to extract features and detect vehicle models, and the second model which is the main contribution of this article is based on YOLO (You Only Look Once) algorithm and SSD to detect vehicle location in the image. To train and test this approach, we collect 150,000 images of 115 domestic and foreign vehicle classes from Iranian websites. Hence, the images have large variations in image size, illumination and pose. The experimental results on the accrued dataset shows that the proposed method is able to correctly classify 91% of the vehicles in uncontrolled conditions.

title: A Nighttime Vehicle Detection Method Based on YOLO v3
abstract: For the past several decades, the number of vehicles has been increased for many times, leading to a lot of traffic issues. The nighttime accident rate is much higher than that in daytime because of the weak light and the uneven distribution of nighttime brightness. Thus, in this paper an effective nighttime vehicle detection approach is designed. First, the original nighttime images were enhanced by an optimal MSR algorithm. Then, a pretrained YOLO v3 network was selected and fine tuned by the enhanced images. Finally, the detection network was used to detect vehicles from the nighttime images and outperformed two widely used object detection methods, namely the Faster R CNN and SSD, on the precision and detection efficiency. The average precision of the proposed method reaches 93.66% which is 6.14% and 3.21% higher than that of the Faster R CNN and SSD, respectively.

title: A Real Time Wrong Way Vehicle Detection Based on YOLO and Centroid Tracking
abstract: Wrong way driving is one of the main causes of road accidents and traffic jam all over the world. By detecting wrong way vehicles, the number of accidents can be minimized and traffic jam can be reduced. With the increasing popularity of real time traffic management systems and due to the availability of cheaper cameras, the surveillance video has become a big source of data. In this paper, we propose an automatic wrong way vehicle detection system from on road surveillance camera footage. Our system works in three stages: the detection of vehicles from the video frame by using the You Only Look Once (YOLO) algorithm, track each vehicle in a specified region of interest using centroid tracking algorithm and detect the wrong way driving vehicles. YOLO is very accurate in object detection and the centroid tracking algorithm can track any moving object efficiently. Experiment with some traffic videos shows that our proposed system can detect and identify any wrong way vehicle in different light and weather conditions. The system is very simple and easy to implement.

title: Real time vehicle detection and counting based on YOLO and DeepSORT
abstract: Intelligent vehicle detection and counting are becoming increasingly important in the field of highway and transport infrastructure management. Traditional methods based on image information have shown several limitations. Especially in real world environment conditions, real time detection, classification and counting each type of vehicle are still a big challenge. The main purpose of this study is to develop an adaptive model that combine YOLOv4 and DeepSORT. The new model can detect object with high accuracy and fast calculation time by taking the benefits of tracking with a focus on simple, effective algorithms. Experiment results have shown that our proposed approach outperforms the original one at least 11% of AP and 12% of AP50 for most field scenarios of our dataset at a real time speed of ~32 FPS.

title: Analysis of Ex YOLO Algorithm with Other Real Time Algorithms for Emergency Vehicle Detection
abstract: This paper highlights the topic of object recognition and its algorithms, used in detection of different objects. By using the concept of object recognition, emergency vehicles like ambulances, fire trucks and ambulances can be identified on the way so that a clear path can be made for them. This can be used in autonomous vehicles as an algorithm to identify emergency vehicles. This paper is an extension of a previous paper (Goel et al. in Intelligent communication, control and devices. Springer, Singapore  highlighting present techniques related to object detection. In this paper, the focus is to develop a machine learning model that is built on YOLO algorithm and is able to recognize emergency vehicles. Model proposed is Extended YOLO (Ex YOLO) an extension of YOLO. It is basically an ML pipeline with two phases, one is the YOLO algorithm that creates bounding boxes across objects and the other would be the classifier that would detect emergency vehicles. The output from the YOLO algorithm is cropped out and then preprocessed. Image tensors are then passed to Phase II classifiers that are pretrained and classified the images to further categories. Classifiers present in Phase II of the pipeline work independently.

title: FAST AND ACCURATE DETECTION OF ROAD TO CONTROL THE VEHICLE MOVEMENT USING HAAR LIKE FEATURES AND YOLO CLASSIFIER
abstract: Autonomous driving is the most focused research problem in real world to ensure the transportation services to the people. In this research work, autonomous driving is focused by introducing the techniques which can ensure the uninterrupted vehicle movement in the road even with presence of obstructs. This is ensured by introducing the technique Fast and Accurate Road Detection System (FARDS) In the proposed research method, initially preprocessing is done on the traffic surveillance video to remove the noises by applying the Hybrid Median Filter method. And then background extraction is done by using markov random field process to extract the required objects. After extracting the required objects from the videos, Haar like Feature extraction is done to detect the objects present in the videos. Based on those feature road and objects detection is performed dynamically by applying Yolo classifier. This information is utilized to adjust the vehicle speed, acceleration and steering values to ensure the uninterrupted vehicle movements without any accidents. The overall evaluation of the research method is done in the matlab simulation environment from which it is proved that the proposed method FARDS leads to ensure the better outcome than the existing research techniques.

title: Embedded System Real Time Vehicle Detection based on Improved YOLO Network
abstract: Real time detection of the pavement environment is an important part of autonomous driving technology. This paper presents a real time vehicle detection system based on embedded devices. Based on the existing yolov3 tiny neural network structure, this paper proposes a new neural network structure the YOLO v3 live. Tailoring the network layer structure of YOLOv3 tiny, and quantify the network parameters in the network. Reducing the complexity of computing in embedded devices, making the proposed neural network structure more suitable for embedded devices. The new structure is tested, before quantization the YOLO v3 live's detection precision can achieve 87.79% mAP on the test set, after quantization of network parameters can achieve 69.79% mAP and detection speed can achieve 28 FPS.

title: Detection of vehicle with Infrared images in Road Traffic using YOLO computational mechanism