Real-time Vehicle Detection Algorithm Research Articles

Vehicle detection algorithm based on lightweight YOLOX

Nowadays, accurate and fast vehicle detection technology is of great significance for constructing intelligent transportation systems in the context of the era of big data. This paper proposes an improved lightweight YOLOX real-time vehicle detection algorithm. Compared with the original network, the detection speed and accuracy of the new algorithm have been improved with fewer parameters. First, referring to the GhostNet, we make a lightweight design of the backbone extraction network, which significantly reduces the network parameters, training cost, and inference time. Furthermore, by introducing the α-CIoU loss function, the regression accuracy of the bounding box (bbox) is improved, while the convergence speed of the model is also accelerated. The experimental results show that the mAP of the improved algorithm on the BIT-Vehicle dataset can reach up to 99.21% with 41.2% fewer network parameters and 12.7% higher FPS than the original network and demonstrate the effectiveness of our proposed method.

Real-time vehicle detection algorithm based on a lightweight You-Only-Look-Once (YOLOv5n-L) approach

A vehicle detection algorithm is of great significance for automatic driving technology. Current vehicle detection algorithms suffer from the complex structure, high configuration of hardware requirements, and the difficulty to apply to mobile terminal equipment. In order to solve these issues, this paper proposes an improved YOLOv5 algorithm, named YOLOv5n-L, for lightweight. First, a depthwise separable convolution and a C3Ghost module are used to replace several C3 modules to reduce the model parameters and improve the detection speed. Then a Squeeze-and-Excitation attention mechanism is integrated into backbone network to improve the accuracy of the algorithm and suppress the environmental interference. Finally, a bidirectional feature pyramid network is used for multi-scale feature fusion to enrich feature information and improve the feature extraction ability of the proposed algorithm. The experimental results demonstrate that compared with the original algorithm, the model weight is reduced by 40 % to only 2.3 M. The mean average precision (mAP@0.5) is increased by 1.7 %. The detection speed reaches 80 FPS, which could accurately detect vehicle targets in real-time.

A Novel System for Nighttime Vehicle Detection Based on Foveal Classifiers With Real-Time Performance

Vehicle monitoring using camera networks is an important task for traffic applications. Moreover, it becomes critical in nighttime, when the probability of an accident considerably increases as visibility conditions worsen. Typical approaches are mostly based on the assumption that regions delimiting vehicle lights are well defined, so that they are segmented and then associated to vehicle entities. However, this assumption fails in images acquired by existing traffic camera networks, where vehicle lights are revealed as flashes and other complex light patterns, occupying large and even disconnected image regions. In this work, a real-time vehicle detection algorithm for nighttime situations has been presented, which is able to locate vehicles in the image by analyzing the previous complex light patterns. For this purpose, a novel machine learning framework based on a grid of foveal classifiers has been designed. Every classifier in the grid processes the same global image descriptor (only one descriptor is computed per image). However, every one of them is trained to predict a different output depending on the classifier position in the grid and the vehicle ground-truth location. Additionally, only point-based annotations are required to train the grid of foveal classifiers, speeding up the cost of creating the required databases. Experimental results prove the effectiveness of the proposed method in a new created nighttime database with point-based annotations.

LittleYOLO-SPP: A delicate real-time vehicle detection algorithm

Vehicle detection in real-time is a challenging and important task. The existing real-time vehicle detection lacks accuracy and speed. Real-time systems must detect and locate vehicles during criminal activities like theft of vehicle and road traffic violations with high accuracy. Detection of vehicles in complex scenes with occlusion is also extremely difficult. In this study, a lightweight model of deep neural network LittleYOLO-SPP based on the YOLOv3-tiny network is proposed to detect vehicles effectively in real-time. The YOLOv3-tiny object detection network is improved by modifying its feature extraction network to increase the speed and accuracy of vehicle detection. The proposed network incorporated Spatial pyramid pooling into the network, which consists of different scales of pooling layers for concatenation of features to enhance network learning capability. The Mean square error (MSE) and Generalized IoU (GIoU) loss function for bounding box regression is used to increase the performance of the network. The network training includes vehicle-based classes from PASCAL VOC 2007, 2012 and MS COCO 2014 datasets such as car, bus, and truck. LittleYOLO-SPP network detects the vehicle in real-time with high accuracy regardless of video frame and weather conditions. The improved network achieves a higher mAP of 77.44 % on PASCAL VOC and 52.95 % mAP on MS COCO datasets.

Real-Time Vehicle Detection Algorithm Based on Vision and Lidar Point Cloud Fusion

Vehicle detection is one of the most important environment perception tasks for autonomous vehicles. The traditional vision-based vehicle detection methods are not accurate enough especially for small and occluded targets, while the light detection and ranging- (lidar-) based methods are good in detecting obstacles but they are time-consuming and have a low classification rate for different target types. Focusing on these shortcomings to make the full use of the advantages of the depth information of lidar and the obstacle classification ability of vision, this work proposes a real-time vehicle detection algorithm which fuses vision and lidar point cloud information. Firstly, the obstacles are detected by the grid projection method using the lidar point cloud information. Then, the obstacles are mapped to the image to get several separated regions of interest (ROIs). After that, the ROIs are expanded based on the dynamic threshold and merged to generate the final ROI. Finally, a deep learning method named You Only Look Once (YOLO) is applied on the ROI to detect vehicles. The experimental results on the KITTI dataset demonstrate that the proposed algorithm has high detection accuracy and good real-time performance. Compared with the detection method based only on the YOLO deep learning, the mean average precision (mAP) is increased by 17%.

Real‐time vehicle detection with foreground‐based cascade classifier

The strategy based on Haar-like features and the cascade classifier for vehicle detection systems has captured growing attention for its effectiveness and robustness; however, such a vehicle detection strategy relies on exhaustive scanning of an entire image with different sizes sliding windows, which is tedious and inefficient, since a vehicle only occupies a small part of the whole scene. Therefore, the authors propose a real-time vehicle detection algorithm which is based on the improved Haar-like features and combines motion detection with a cascade of classifiers. They adopt a visual background extractor, accompanied by morphological processing, to obtain foregrounds. These foregrounds retain vehicle features and provide the positions within images where vehicles are most likely to be located. Subsequently, vehicle detection is performed only at these positions by using a cascade of classifiers instead of a single strong classifier, which is able to improve the detection performance. The authors' algorithm has been successfully evaluated on the public datasets, which demonstrates its robustness and real-time performance.