Restricted by the performance and resources of embedded platform,the vehicle detection algorithm based on deep learning is faced with problems such as large number of network parameters,complex model and difficult migration during deployment.In this paper,an improved target detection algorithm of YOLOv3 based on MobileNetv3 network is proposed.The light-weight MobileNetv3 network is used to replace the traditional backbone feature extraction network Darknet53,the FPN feature pyramid is modified to FPN+PAN structure,and the attention mechanism is introduced to improve the detection accuracy of the algorithm.The experimental results on computer platform and RV1126 embedded platform show that the improved YOLOv3 algorithm reduces the model size by 50%,improves the detection accuracy by 0.85%,and improves the inference time by 50%.
Key words
MobileNetv3 /
YOLOv3 /
real-time vehicle detection /
attention mechanism
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
References
[1] Redmon J,Divvala S,Girshick R,et al.You Only Look Once: Unified, Real-Time Object Detection[C]//Computer Vision & Pattern Recognition.IEEE,2016.
[2] 顾晋,罗素云.基于改进的YOLOv3车辆检测方法[J].农业装备与车辆工程,2021,59(7):6.
[3] 王硕,王孝兰,王岩松,等.基于改进tiny-YOLOv3的车辆检测方法[J].计算机与数字工程,2021,49(8):6.
[4] 王玲,张松,王鹏,等.基于MS-YOLOv3的车辆目标实时检测算法[J].计算机应用与软件,2021,38(10):7.
[5] Howard A G,Zhu M,Chen B,et al.MobileNets:Efficient Convolutional Neural Networks for Mobile Vision Applications[EB/OL].[2022-11].https://blog.csdn.net/weixin_36670529/article/details/102768196.
[6] 张达为,刘绪崇,周维,等.基于改进YOLOv3的实时交通标志检测算法[J].计算机应用,2022,42(7):8.
[7] Redmon J,Farhadi A.YOLOv3:An Incremental Improvement[J].arXiv e-prints,2018.
[8] He K,Zhang X,Ren S,et al.Deep Residual Learning for Image Recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).IEEE,2016.
[9] Redmon J,Farhadi A.YOLO9000:Better, Faster, Stronger[C]//IEEE Conference on Computer Vision & Pattern Recognition.IEEE,2017:6517-6525.
[10] Howard A,Sandler M,Chu G,et al.Searching for MobileNetV3[EB/OL].[2022-11].https://blog.csdn.net/hzhj2007/article/details/79731888.
[11] Jie H,Li S,Gang S,et al.Squeeze-and-Excitation Networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2017(99).
[12] Liu S,Qi L,Qin H,et al.Path Aggregation Network for Instance Segmentation[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).IEEE,2018.
PDF(2018 KB)
