关键词: 车辆检测, YOLOv3, 卷积神经网络, Darknet53, [k]-means

Abstract:

Real-time detection of road vehicles is a hot topic in the field of computer vision. Aiming at the problems of low detection accuracy and slow speed of road vehicle detection algorithms, a road vehicle target detection method based on improved YOLOv3 is proposed. Firstly, a convolutional neural network with 30 convolutional layers is constructed by improving the Darknet53 skeleton network, which reduces the network cost and improves the detection speed. Secondly, according to the characteristics of road vehicle aspect ratio fixed, the [k]-means clustering method is used to select the anchor point prediction bounding box, which improves the detection speed and accuracy. The experimental results show that the proposed method has an average accuracy of 90.08% on the standard dataset KITTI, which is 0.47% higher than the traditional YOLOv3, and the detection speed reaches 76.04 f/s, which is obviously superior to the traditional YOLOv3 algorithm. At the same time, the proposed method is applied to the vehicle driving dynamic data set, which can realize real-time detection of road vehicles in the video.

Key words: vehicle detection, YOLOv3, Convolutional Neural Network （CNN）, Darknet53, [k]-means