面向铝合金焊缝DR图像缺陷的Sim-YOLOv8目标检测模型  

Sim-YOLOv8 Object Detection Model for DR Image Defects in Aluminum Alloy Welds

作　　者：吴磊 储钰昆 杨洪刚[2] 陈云霞 Wu Lei;Chu Yukun;Yang Honggang;Chen Yunxia(School of Intelligent Manufacturing and Control Engineering,Shanghai Polytechnic University;Shanghai Dianji University)

机构地区：[1]上海第二工业大学智能制造与控制工程学院,上海201209 [2]上海电机学院,上海201306

出　　处：《中国激光》2024年第16期21-30,共10页Chinese Journal of Lasers

基　　金：国家自然科学基金(51809161);上海市自然科学基金(18ZR1416000)。

摘　　要：针对当前目标检测算法在铝合金焊缝数字射线成像(DR)图像缺陷检测中精度不足的问题,提出了一种基于YOLOv8的改进模型Sim-YOLOv8。首先改进C2f,通过增加SimAM模块提升模型的整体性能;其次,针对部分像素较小的气孔和夹渣缺陷,将首层卷积模块更换为Focus模块,以提升模型对小目标的检测能力;最后添加WIoU损失函数,以提高模型锚框的质量,从而提高检测效果。实验结果表明:在阈值为0.5的前提下,Sim-YOLOv8模型对气孔、夹杂、未焊透这三类缺陷检测的平均精度(mAP@0.5)达到了93.6%、94.4%、97.3%,较原模型分别提高了2.5、1.9和1.7个百分点,具有更好的焊缝缺陷检测效果。Objective Owing to the influence of manufacturing processes and welding environments,aluminum alloy materials,are prone to various internal welding defects during the welding process,such as pores,slag inclusions,and incomplete penetration.Currently,defects in DR(digital radiography)weld seam images are typically manually identified by trained professionals.However,the manual detection of DR ray film defects has a high workload,low efficiency,and problems with false positives and missed detection.With the rapid development of computer and digital image-processing technologies,deep learning is widely used in object recognition.The current target detection algorithms exhibit sub-optimal performance in accurately detecting weld defects.Furthermore,enhancement of the detection accuracy of the model often comes at the cost of decreased speed and increased parameter count.This in turn hinders effective deployment.To address this issue in the defect detection of aluminum alloy weld DR images,a lightweight weld defect detection algorithm based on YOLOv8 is proposed.This improved algorithm effectively resolves the problems associated with increased parameter counts and reduced detection speeds resulting from model enhancement.Methods First,the SimAM module was added to C2f to improve the overall network performance.The specific approach is introducting the SimAM module into the bottleneck module of the C2f module(Fig.4).This can improve the feature expression ability of the module without increasing the number of model parameters.The loss function was then replaced with the WIoU loss function to improve the quality of the anchor frame,and the first-layer convolution module was replaced with the Focus convolution module to increase the detection speed while increasing the network sensory field.These improved the detection effect on small targets.The YOLOv8 model underwent consistent parameter and indicator during model enhancement.This in turn ensured the effectiveness of the improvement points by comparing all indicators

关 键 词：激光技术 图像处理 DR图像缺陷检测 YOLOv8 SimAM模块 WIoU损失函数 

分 类 号：TG441.7[金属学及工艺—焊接]