Car front detection algorithm based on improved YOLOv5s
余国豪 1贾玮迪 1余鹏飞 1李海燕 1李红松1
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作者信息
1. 云南大学信息学院,云南昆明 650500
折叠
摘要
针对车检站中车辆检测的实际需求,提出一种改进YOLOv5s的轻量级车脸检测算法.使用ShuffleNetV2网络作为Backbone,在保证模型检测精度的同时实现模型的轻量化与实时性;将通道-空间注意力(SA-Net)与跨通道注意力(Triplet)相结合,提出一种跨通道-空间注意力模块(SA-Triplet attention,STA),提高模型的检测精度;提出一种基于STA注意力模块的跨层特征融合模块(SA-Triplet attention feature fusion,STA-FF),进一步提高模型的检测精度.在自建车脸检测数据集Car-Data上进行实验,所提模型的平均检测精度达到了 94.3％,检测速度达到了 105.60 FPS,模型参数量为12.36 M.
Abstract
Aiming at the actual demand of vehicle detection in vehicle inspection station,a lightweight car front detection algo-rithm was improved by YOLOv5s was proposed.ShuffleNetV2 network was used as Backbone to realize the lightweight and real-time of network model while ensuring the accuracy of model detection.A SA-Triplet attention module(STA)was proposed by combining SA-Net and Triplet Attention to improve the detection accuracy of the model.A cross-layer feature fusion module(STA-FF)based on STA attention module was proposed to further improve the detection accuracy of the model.Experiments were carried out on Car-Data,a self-built car front detection dataset,the average detection accuracy of the proposed model reaches 94.3％,the detection speed reaches 105.60 FPS,and the parameters of model are 12.36 M.
关键词
车辆检测/YOLOv5s/轻量级/车脸检测/ShuffleNetV2/注意力机制/跨层特征融合
Key words
vehicle detection/YOLOv5s/lightweight/car front detection/ShuffleNetV2/attention mechanism/cross-layer fea-ture fusion
维普
万方数据