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作 者:刘雷 王野 田长河 丁佳毅 董帆 万书亭[2] LIU Lei;WANG Ye;TIAN Chang-he;DING Jia-yi;DONG Fan;WAN Shu-ting(China Power Investment Jinzhou Port Co.,Ltd.,Jinzhou 121007,China;Hebei Key Laboratory of Electric Machinery Health Maintenance&Failure Prevention,North China Electric Power University,Baoding 071003,China)
机构地区:[1]中电投锦州港口有限责任公司,锦州121007 [2]华北电力大学河北省电力机械装备健康维护与失效预防重点实验室,保定071003
出 处:《科学技术与工程》2025年第4期1563-1572,共10页Science Technology and Engineering
基 金:国家自然科学基金(52275109);河北省自然科学基金(E2022502007)。
摘 要:能否准确识别火车车钩的连接手柄对摘钩机器人的工作具有重大意义。针对这一问题,提出一种基于改进YOLOv5的火车连接手柄目标识别算法,在主干特征提取网络中融入C_SAC(C_switchable atrous convolution)模块,并引入WIOU (wise intersection over union)函数作为新的边界框损失函数,增强主干网络的特征提取能力,提高模型的泛化能力和收敛速率。然后采集生产现场不同环境、不同位置下的火车车钩连接手柄图片进行识别,实验结果表明改进的YOLOv5算法对火车车钩连接手柄的目标识别率达到96.6%,相比于原始算法效果在准确率、召回率、平均精度等方面均有较大提升。最后应用于研制的火车车厢自动摘钩机器人中,极大提高了自动摘钩的准确性和有效性。Accurately identifying the connecting handle of the train coupler is of great significance for the operation of the uncoupling robot.A train connection handle target recognition algorithm based on improved YOLOv5 was proposed to address this issue.The C_switchable atrous convolution(C_SAC)module was integrated into the backbone feature extraction network,and the wise intersection over union(WIOU)function was introduced as a new bounding box loss function to enhance the feature extraction ability of the backbone network,improve the model s generalization ability and convergence rate.Then,images of the connecting handles of train couplers in different environments and positions on the production site were collected for recognition.The experimental results show that the improved YOLOv5 algorithm achieves a target recognition rate of 96.6%for the connecting handles of train couplers.Compared with the original algorithm,it shows significant improvements in accuracy,recall,average accuracy,and other aspects.Finally,it was applied in the development of an automatic uncoupling robot for train carriages,greatly improving the accuracy and effectiveness of automatic uncoupling.
关 键 词:摘钩机器人 火车车钩 连接手柄 目标识别 YOLOv5
分 类 号:TP391.41[自动化与计算机技术—计算机应用技术]
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