基于目标检测的焊接电弧形态在线定量检测

On-line quantitative detection of welding arc shape based on object detection

超窄间隙焊接过程中电弧形态难以在线监测和定量描述,为此提出基于YOLOv5s的轻量化目标检测网络YOLO-SEC.设计3种轻量化卷积模块AD-Shufflenet、LC3和Conv-Maxpool,应用于基线网络的骨干网络和颈网络;在检测头网络中使用单检测层推理结构,有效降低网络复杂度.设计并使用k-means+算法聚类数据集的已标注锚框,得到网络训练所需的预设锚框尺寸.引入EIoU定位损失函数,提升网络的准确率.将单目线性相机标定算法引入预测框绘制函数,在焊接前通过标定将像素长度转化为实际长度,实现电弧尺寸的定量描述.实验结果表明,YOLO-SEC的检测精度达到99.2%、测量误差小于0.5%、推理速度为3.1 ms、网络体积为0.6 MB,以上指标均优于YOLOv5s网络和同类型轻量化网络.

A lightweight object detection network YOLO-SEC based on YOLOv5s was proposed to resolve the problem that the arc shape in ultra-narrow gap welding process was difficult to detect online and describe quantitatively.Three lightweight convolutional modules,namely AD-Shufflenet,LC3 and Conv-Maxpool,were designed and applied to the backbone network and neck network of baseline network.Single detection layer inference structure was used in the detection head network,so that the network complexity was effectively reduced.The k-means+algorithm was designed to cluster labeled anchor frames in the data set and obtain the preset anchor frame size required for network training,and the EIoU positioning loss function was introduced to improve the accuracy of the network.The monocular linear camera calibration algorithm was introduced into the prediction frame drawing function,so that the pixel length could be converted into the actual length by calibration before welding,and the quantitative description of arc size was realized.Experimental results showed that the detection accuracy of YOLO-SEC was 99.2%,the measurement error was less than 0.5%,the reasoning speed was 3.1 ms,and the network volume was 0.6 MB.All the above indexes were better than the YOLOv5s network and the same type of lightweight network.