Automatic and fast weld type classification, tacked spot recognition and weld ROI (region of interest) determination are key links for intelligent welding robot, because they directly affect the seam tracking and welding parameters such as current, voltage and torch inclination. Nevertheless, there are few studies on weld type classification, tacked spot recognition, and weld ROI determination. Considering the fast inference speed of YOLOv5, an automatic weld type classification, tacked spot recognition and weld ROI determination based on modified YOLOv5 is presented in this paper. First, the detection requirements of weld type classification, tacked spot recognition and weld ROI determination are transformed into a unified target localization task to improve the inference speed, so the three results can be obtained through single inference; the next, to improve the localization accuracy of weld ROI, the center component bias between the predicted box and ground truth is added to the original CIOU localization loss function; then, a weighted classification loss function is used to reduce the false positives in fillet and groove welds; finally, a self-template method for padding image border is presented to improve the generalization ability of the trained model. Experimental results show that: the presented method reaches 100% precision, 100% recall, 0.91 mean intersection-over-unio, 2.41 pixels center component bias of determined weld ROI and 18 ms inference times in the original size images; the center component bias of determined weld ROI is reduced from 2.38 pixels to 2.18 pixels by adding the center component bias loss to CIOU function in the padded images; the weighted classification loss function reduced the false positives in fillet and groove welds; compared with the default gray border padding method, the model trained by using self-template padding method reduced the center component bias of the determined weld ROI from 2.73 pixels to 2.41 pixels in the original size images. Moreover, when Ref. Chen et al. (2022) positions the welding seam coordinates in the weld ROI determined by the presented method, the recall is improved from 0.96 to 0.98, and the computation time is reduced from 180 ms to 48 ms.

自动快速焊接类型分类、定位点识别和焊接 ROI（感兴趣区域）确定是智能焊接机器人的关键环节，因为它们直接影响焊缝跟踪和电流、电压和焊枪倾角等焊接参数。然而，关于焊接类型分类、定位点识别和焊接 ROI 确定的研究很少。考虑到 YOLOv5 的快速推理速度，本文提出了一种基于改进的 YOLOv5 的自动焊接类型分类、定位点识别和焊接 ROI 确定。首先，将焊缝类型分类、定位点识别和焊缝ROI确定的检测需求转化为统一的目标定位任务，以提高推理速度，因此可以通过一次推理获得三个结果；接下来，为了提高焊缝ROI的定位精度，将预测框和ground truth之间的中心分量偏差添加到原始CIOU定位损失函数中；然后，使用加权分类损失函数来减少角焊缝和坡口焊缝中的误报；最后，提出了一种填充图像边界的自模板方法，以提高训练模型的泛化能力。实验结果表明：所提出的方法在原始尺寸图像中达到了 100% 的精度、100% 的召回率、0.91 的平均交叉联合、2.41 像素的中心分量偏差和 18 ms 的推理时间；通过将中心分量偏差损失添加到填充图像中的 CIOU 函数，确定的焊接 ROI 的中心分量偏差从 2.38 像素减少到 2.18 像素；加权分类损失函数减少了角焊缝和坡口焊缝中的误报；与默认的灰色边框填充方法相比，使用自模板填充方法训练的模型将原始尺寸图像中确定的焊缝ROI的中心分量偏差从2.73像素减少到2.41像素。此外，当 Ref. 陈等。(2022) 将焊缝坐标定位在所提出方法确定的焊接 ROI 中，召回率从 0.96 提高到 0.98，计算时间从 180 毫秒减少到 48 毫秒。原始大小图像中的 41 像素。此外，当 Ref. 陈等。(2022) 将焊缝坐标定位在所提出方法确定的焊接 ROI 中，召回率从 0.96 提高到 0.98，计算时间从 180 毫秒减少到 48 毫秒。原始大小图像中的 41 像素。此外，当 Ref. 陈等。(2022) 将焊缝坐标定位在所提出方法确定的焊接 ROI 中，召回率从 0.96 提高到 0.98，计算时间从 180 毫秒减少到 48 毫秒。