基于深度学习的焊缝图像识别方法研究

Research on weld image recognition method based on deep learning

随着工业技术的发展,焊接机器人被广泛应用于现代工业中的焊接工作,使焊接作业更加自动化和智能化。然而,受焊接环境、材料属性和技术的限制,焊接机器人作业后产生的焊缝存在各种隐患和问题。为了准确识别焊缝类型并加以改进,研究人员利用人工智能算法和技术进行处理,特别是深度学习模型使视觉识别方面的准确度不断提高,成为了主流方法。基于深度学习,文章研究焊缝识别方法,选用卷积系列网络、自注意力网络和Transformer模型进行焊缝识别工作,为焊缝识别问题提供了新的解决方案。

With the development of industrial technology,welding robots have become the main force of welding work in modern industry,turning welding operation into an automatic and intelligent process.However,welding seams produced by welding robots are often limited by the welding environment,welding material properties and welding process parameters.Therefore,it is necessary to use image processing technology to identify welds accurately.Based on this starting point,this paper conducts an in-depth study of deep learning-based weld identification methods,such as convolutional series network,self-attention network and the current hot Transformer model for weld identification work,to obtain ideal results and provide a new solution to the problem of weld identification.