一种基于YOLOv5s的改进装甲目标检测算法  

An Improved Armored Target Detection Algorithm Based on YOLOv5s

作　　者：易图明 王先全[2] 袁威[1] 孔庆勇 YI Tuming;WANG Xianquan;YUAN Wei;KONG Qingyong(Southwest Computer Co.,Ltd.,Chongqing 400060,China;Chongqing University of Technology,Chongqing 400054,China)

机构地区：[1]西南计算机有限责任公司,重庆400060 [2]重庆理工大学,重庆400054

出　　处：《现代信息科技》2023年第5期73-77,共5页Modern Information Technology

摘　　要：针对装甲目标图像背景复杂、目标尺度小等问题,提出一种基于YOLOv5s的装甲目标检测算法。首先在FPN结构中增加一个浅层分支,增强对小目标特征的提取能力;其次通过Focal Loss损失函数来平衡正负样本;再次将CIoU_loss用作边框回归损失函数,用以提升识别精度;最后将ECA注意力模块引入算法中,加强重要特征的表达。实验结果表明,改进算法在自制数据集上AP达到92.9%,相较于原始算法提高了4.2%,能够很好地满足装甲目标检测任务的精度与速度需求。Aiming at the problems of complex background and small target scale of armored target image,an armored target detection algorithm based on YOLOv5s is proposed.First,a shallow branch is added to the FPN structure to enhance the ability of extracting small target features;Secondly,the Focal Loss loss function is used to balance the positive and negative samples;CIoU_Loss is used as the loss function of frame regression to improve the recognition accuracy;Finally,ECA attention module is introduced into the algorithm to enhance the expression of important features.The experimental results show that AP of the improved algorithm on the self-made data set achieves 92.9%,which is 4.2%higher than that of the original algorithm,and can well meet the accuracy and speed requirements of the armored target detection task.

关 键 词：装甲目标 YOLOv5s 特征金字塔 ECA注意力模块 Focal_loss 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]