Improved YOLOv3 Vehicle Detection Algorithm Embedded in Dilated Convolution Module

Abstract

Abstract: Vehicle detection on image or video data is one of the hotspots in the field of computer vision, and it is also an important part of intelligent transportation systems. In view of the complex and changeable vehicle detection scenes and the existing vehicle detection algorithms can not meet the requirements of high precision and high real-time at the same time, this paper proposes an improved YOLOv3 vehicle detection algorithm and builds its own vehicle detection data set. First, we embed the dilated convolution module in the original and feature extraction network Darknet-53 to reduce the loss of target information and enhance the receptive field. Secondly, in the NMS (non-maximum suppression) module, in order to reduce the missed detection, this article discusses the traditional NMS and makes improvements. If the IoU of the prediction frame is greater than the set threshold, it will be attenuated in a certain way. The improved method shows better performance than other algorithms on the KITTI standard data set, and the verification accuracy can reach 96% in the self-built data set, and the detection speed is 25.9 frames/s.

Key words: vehicle detection, real-time detection, dilated convolution, NMS

HU Chang-ran, FAN Yan-guo, YU Ding-feng. Improved YOLOv3 Vehicle Detection Algorithm Embedded in Dilated Convolution Module[J]. Computer and Modernization, 2021, 0(04): 53-60.

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