关键词: 车辆实时检测, YOLOv3, 目标定位, 卷积神经网络, 深度残差网络, 特征图

Abstract:

Real-time detection and extraction of vehicle information in complex natural scenes have always been one of the important contents in the field of computer vision. The breakthrough of problems in this field can not only bring effective promotion to the realization of auto driving technology, but also have important practical significance in the improvement of real-time parking monitoring system and automatic parking schedule algorithm in parking lot. In order to solve the problems of incomplete vehicle information detection area, low accuracy and inability to locate distant vehicles in real-time vehicle information detection, a new real-time vehicle detection and classification model—Vehicle-YOLO is proposed. Based on the latest YOLOv3 algorithm model, the model improves the accuracy and universality of vehicle real-time information detection by changing image parameters, enhancing features extraction ability of deep residual network, and using 5 different scale convolutional feature maps to extract potential bounding boxes of vehicles. Then, the odel performance is tested and analyzed with datasets, such as KITTI and VOC. Experimental results indicate that the Vehicle-YOLO model achieves 96% mean average accuracy and transmission speed about 40 f/s on KITTI dataset, in other words, the model can still maintain a better real-time detection speed when detection accuracy is improved. In addition, the experimental results of Vehicle-YOLO on other datasets such as VOC also show different degrees improvement of accuracy, which demonstrate that the proposed model has better universality and performance in common object positioning detection than traditional algorithms model of object detection.

Key words: real-time vehicle detection, YOLOv3, target detection, convolutional neural network, deep residual network, feature map