改进的轻量级YOLOv5算法在行人检测的应用

Application of Improved Lightweight YOLOv5 Algorithm in Pedestrian Detection

【目的】目前,行人检测算法存在模型复杂、检测精度较低、检测速度慢的问题。为了解决这些问题,将YOLOv5算法进行了改进,能够更好地应用于行人检测。【方法】首先使用深度可分离卷积替换YOLOv5算法骨干网络中的普通卷积,降低了模型的计算量和参数量,提高模型的检测效率;然后在骨干网络的特征融合部分添加通道注意力和空间注意力机制,让网络关注于图像中行人的位置信息和通道信息;最后使用EIOU损失函数优化训练模型,并使用K-means++聚类算法来生成先验框。【结果】将改进后的模型在INRIA行人检测数据集上与其他算法进行了对比实验。结果表明,改进后的模型精确度达到89%,相比于原模型提高了7.6%,检测速度达到每秒106帧。【结论】本文改进算法提高了行人检测的速度和精度,且模型数据量小,易于实时检测和部署。

[Objective]In this paper,we propose an improved YOLOv5 algorithm to address the problems of the high computational complexity of pedestrian detection algorithms,low detection accuracy,and slow detection speed,which can be better applied to pedestrian detection.[Methods]Firstly,the vanilla convolution in the YOLOv5 backbone network is replaced by the depthwise separable convolution,which reduces the number of calculations and parameters while improving detection accuracy.Then,channel attention and spatial attention are incorporated into the feature fusion part of the backbone network,which can force our network to focus on the location and channel information of pedestrians in the image.Finally,the EIOU loss function is used to optimize the proposed model,and the K-means++clustering algorithm is used to generate priori boxes.[Results]The results show our proposed model can achieve a detection accuracy of 89%,which is 7.6%higher than the original backbone,and the detection speed reaches 106 frames per second when using the INRIA pedestrian detection dataset.[Conclusions]Our proposed method significantly improves the speed and accuracy of pedestrian detection,has also small parameters and is easier to detect and deploy in real-time.