In narrow overlap welding, serious defects in the weld will lead to band breakage accident, and the whole hot dip galvanizing unit will be shut down. Laser vision inspection hardware is used to collect real-time image of weld surface, and image defect recognition and evaluation system is developed to real-time detect quality. Firstly, region division is implemented. In view of the characteristics of gray image such as large information, low contrast and blurred edge, an improved image segmentation algorithm is proposed by using image convolution to enhance edge features and combining with integral image, which can quickly and accurately extract the weld edge and divide the region, and the processing time can meet the real-time requirements. Then comparing the effect of traditional method and convolution neural network in identifying defects, VGG16 is used to identify weld defects. In order to ensure real-time performance, a two-stage weld defect recognition is proposed. First, the large defective area is identified, and then, the defect is accurately identified in the area. This method can quickly extract defect areas and complete weld defect classification. Experiments show that the accuracy can reach 97% and average running time takes 3.2 s, meeting the online detection requirements.

在窄搭接焊接中，焊缝中的严重缺陷会导致带断裂事故，并且整个热浸镀锌装置都将关闭。激光视觉检测硬件用于收集焊接表面的实时图像，并开发了图像缺陷识别和评估系统以实时检测质量。首先，实行区域划分。针对灰度图像信息量大，对比度低，边缘模糊等特点，提出了一种改进的图像分割算法，即通过图像卷积增强边缘特征，并与整体图像相结合，可以快速，准确地提取出焊缝边缘。划分区域，处理时间可以满足实时性要求。然后比较传统方法和卷积神经网络在识别缺陷方面的效果，VGG16用于识别焊接缺陷。为了保证实时性，提出了两阶段焊接缺陷识别方法。首先，识别出较大的缺陷区域，然后在该区域中准确识别缺陷。这种方法可以快速提取缺陷区域并完成焊接缺陷分类。实验表明，该算法的准确率可达97％，平均运行时间为3.2 s，满足在线检测要求。