基于YOLOv5的装载机物料细粒度检测优化算法  

Optimization algorithm for fine-grained detection of loader materials based on YOLOv5

作　　者：顾同成 徐武彬[1,2] 李冰 李志恒[2,3] 惠翔禹 何心 GU Tongcheng;XU Wubin;LI Bing;LI Zhiheng;HUI Xiangyu;HE Xin(Mechanical and Automotive Engineering,Guangxi University of Science and Technology,Liuzhou 545006,China;Guangxi Earthmoving Machinery Collaborative Innovation Center,Liuzhou 545006,China;Guangxi Liugong Machinery Co.,Ltd.,Liuzhou 545116,China)

机构地区：[1]广西科技大学机械与汽车工程学院,广西柳州545006 [2]广西土方机械协同创新中心,广西柳州545006 [3]广西柳工机械股份有限公司,广西柳州545116

出　　处：《计算机集成制造系统》2024年第1期239-252,共14页Computer Integrated Manufacturing Systems

基　　金：广西科技计划资助项目(桂科AD22080042);广西科技重大专项资助项目(桂科AA22068064);广西重点研发计划资助项目(桂科AB22035066)。

摘　　要：针对装载机智能铲装过程中缺少对物料细粒度方面的高精度检测算法问题,提出基于YOLOv5改进的物料细粒度目标检测算法。该方法主要利用注意力机制提高模型对细粒度特征的提取能力和对低质量数据的检测能力。为进一步利用注意力优化网络性能,提出双线性注意力机制,研究最佳嵌入方案并将软阈值思想与双线性注意力机制结合,以达到缓解低质量数据对模型检测精度影响的目的。实验结果表明,相较于原YOLOv5,双线性注意力机制改进后的网络在高质量样本上的mAP@0.5为93.2%,提高6.0%,每秒检测帧数(FPS)为52.6;嵌入软阈值后,网络在低质量样本上的mAP@0.5为90.2%,提高9.9%,FPS为50.0,满足装载机智能铲装过程对算法检测精度和实时性的要求。To address the problem of the lack of high precision detection algorithms for material fine grain aspects in the intelligent shoveling process of loaders,an improved material fine grain object detection algorithm based on YOLOv5 was proposed.This method primarily employed an attention mechanism to enhance the model's capability in extracting fine-grained features and detecting low-quality data.To further leverage attention for optimizing network performance,a bilinear attention mechanism was introduced.The optimal embedding scheme was investigated,and the concept of soft thresholding was integrated with the bilinear attention mechanism,aiming to mitigate the impact of low-quality data on the model's detection accuracy.Experimental results demonstrated that compared to the original YOLOv5,the network improved with bilinear attention mechanism achieved a 6.0%increase in mAP@0.5 to 93.2%on high-quality samples,with a Frames Per Second(FPS)of 52.6.After embedding the soft threshold,the network's mAP@0.5 on low-quality samples was improved by 9.9%to 90.2%,with an FPS of 50.0,meeting the requirements for accuracy and real-time performance in the intelligent shovel loading process of loaders.

关 键 词：YOLOv5算法 智能铲装 物料识别 细粒度 注意力机制 目标检测 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TP391.4[自动化与计算机技术—控制科学与工程]