基于可变形卷积和多尺度残差注意力的多光谱行人检测

Multi-spectral Pedestrian Detection Based on Deformable Convolution and Multi-Scale Residual Attention

目前多光谱行人检测算法大多对可见光与红外图像融合方法展开研究,但是充分融合多光谱图像所需的参数量巨大,会导致检测速度降低。针对这一问题,提出了一种基于时效性较高的YOLOv5s的多光谱行人检测算法。为了保证算法的检测速度,选用可见光与红外光通道方向上的合并方法作为网络的输入,并通过对传统算法的改进来提升检测精度。首先,用可变形卷积替换部分标准卷积,增强了网络对不规则形状的特征目标的提取能力;其次,用多尺度残差注意力模块替换网络中的空间金字塔池化模块,减弱了背景对行人目标的干扰,提升了检测精度;最后,通过改变连接方式,增加大尺度特征拼接层,提升了网络的检测最小尺度,提升了网络对小目标的检测效果。实验结果表明,改进后的算法在检测速度上有明显优势,并比原算法的mAP@0.5和mAP@0.5∶0.95分别提升了5.1和1.9个百分点。

At present,most of the multi-spectral pedestrian detection algorithms focus on the fusion methods of visible light and infrared images,but the number of parameters to fully fuse multi-spectral images is huge,resulting in lower detection speed.To solve this problem,we propose a multi-spectral pedestrian detection algorithm based on YOLOv5s with high timeliness.To ensure the detection speed of the algorithm,we select the merging method of visible light and infrared light channel direction as the input of the network,and improve the detection accuracy by improving the traditional algorithm.First,some standard convolution is replaced by deformable convolution to enhance the ability of the network to extract irregular shape feature objects.Second,the spatial pyramid pooling module in the network is replaced by multi-scale residual attention module,which weakens the interference of the background to the pedestrian target and improves the detection accuracy.Finally,by changing the connection mode and adding the large-scale feature splicing layer,the minimum detection scale of the network is increased,and the detection effect of the network for small targets is improved.Experimental results show that the improved algorithm has obvious advantages in detection speed,and improves the mAP@0.5 and mAP@0.5:0.95 by 5.1 and 1.9 percentage points over the original algorithm,respectively.