MobileYOLO: Real-Time Object Detection Algorithm in Autonomous Driving Scenarios

Sensors

Dongli Wang¹  Sijie Wen¹  Jiangnan Meng¹  Yan Zhou¹  Richard Irampaye²  Jinzhen Mu³ 

[1] School of Automation and Electronic Information, Xiangtan University, Xiangtan 411105, China;School of Mathematics and Computational Science, Xiangtan University, Xiangtan 411105, China;Shanghai Aerospace Control Technology Institute, Shanghai 201109, China;

关键词: autonomous driving;    object detection;    real-time;    YOLOv4;    KITTI data set;   

DOI  :  10.3390/s22093349

来源: DOAJ

【 摘 要 】

Object detection is one of the key tasks in an automatic driving system. Aiming to solve the problem of object detection, which cannot meet the detection speed and detection accuracy at the same time, a real-time object detection algorithm (MobileYOLO) is proposed based on YOLOv4. Firstly, the feature extraction network is replaced by introducing the MobileNetv2 network to reduce the number of model parameters; then, part of the standard convolution is replaced by depthwise separable convolution in PAnet and the head network to further reduce the number of model parameters. Finally, by introducing an improved lightweight channel attention modul—Efficient Channel Attention (ECA)—to improve the feature expression ability during feature fusion. The Single-Stage Headless (SSH) context module is introduced to the small object detection branch to increase the receptive field. The experimental results show that the improved algorithm has an accuracy rate of 90.7% on the KITTI data set. Compared with YOLOv4, the parameters of the proposed MobileYOLO model are reduced by 52.11 M, the model size is reduced to one-fifth, and the detection speed is increased by 70%.

【 授权许可】

Unknown