基于改进YOLOv5s的工件识别检测算法  

Workpiece recognition and detection algorithm based on improved YOLOv5s

作　　者：余浪 苗鸿宾[1,2] 苏赫朋 申光鹏 YU Lang;MIAO Hongbin;SU Hepeng;SHEN Guangpeng(School of Mechanical Engineering,North University of China,Taiyuan 030051,CHN;Shanxi Province Deep Hole Machining Center,Taiyuan 030051,CHN)

机构地区：[1]中北大学机械工程学院,山西太原030051 [2]山西省深孔加工工程技术研究中心,山西太原030051

出　　处：《制造技术与机床》2024年第4期153-158,180,共7页Manufacturing Technology & Machine Tool

基　　金：重型机械关重传载件机器人智能超声打磨关键技术研究(YDZJSX2022A032)。

摘　　要：针对机器人在抓取目标工件的过程中由于光线强度变化、图像环境复杂和拍摄设备移动等造成的工件识别精度低的问题,文章提出一种改进YOLOv5s的工件识别检测算法。首先,通过数据增强扩充数据集并进行预处理;其次,使用改进的k-means聚类算法重新生成更有效的预设锚框,缩短收敛路径;然后,在特征融合网络中添加CBAM注意力机制,有效抑制背景信息干扰,提高特征提取速度;此外,将特征融合模块中原有的特征金字塔结构替换成加权双向特征金字塔Bi-FPN结构,实现高效的加权特征融合和双向跨尺度连接,提高网络对不同尺度特征的融合效率;最后,通过采用α-IoU作为边界框回归损失函数,提高模型的定位效果。结果表明,改进后的YOLOv5s算法对工件检测的mAP值提升了6.03%,检测速度提升了13.7 fps,验证了改进算法的有效性。Aiming at the low accuracy of workpiece recognition caused by the change of light intensity,the complexity of image environment and the movement of shooting equipment,an improved YOLOv5s workpiece recognition and detection algorithm was proposed.Firstly,the data set is expanded by data enhancement and preprocessed.Secondly,the improved k-means clustering algorithm is used to re-generate a more effective pre-set anchor frame and shorten the convergence path.Then,CBAM attention mechanism is added to the feature fusion network to effectively suppress background information interference and improve feature extraction speed.In addition,the original feature pyramid structure of the feature fusion module is replaced by the weighted bidirectional feature pyramid Bi-FPN structure to achieve efficient weighted feature fusion and bidirectional cross-scale connection,and improve the fusion efficiency of different scale features.Finally,the positioning effect of the model is improved by usingα-IoU as the bounding box regression loss function.The results show that the improved YOLOv5s algorithm improves the mAP value of workpiece detection by 6.03%and the detection speed by 13.7 fps,which verifies the effectiveness of the improved algorithm.

关 键 词：YOLOv5s K-MEANS CBAM Bi-FPN 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]