A Weld Surface Defect Recognition Method Based on Improved MobileNetV2 Algorithm

Abstract

Traditional welding quality inspection methods for pipelines and pressure vessels are time-consuming, labor-intensive, and suffer from false and missed inspection problems. With the development of smart manufacturing, there is a need for fast and accurate in-situ inspection of welding quality. Therefore, detection models with higher accuracy and lower computational complexity are required for technical support. Based on that, an in-situ weld surface defect recognition method is proposed in this paper based on an improved lightweight MobileNetV2 algorithm. It builds a defect classification model with MobileNetV2 as the backbone of the network, embeds a Convolutional Block Attention Module (CBAM) to refine the image feature information, and reduces the network width factor to cut down the number of model parameters and computational complexity. The experimental results show that the proposed weld surface defect recognition method has advantages in both recognition accuracy and computational efficiency. In summary, the method in this paper overcomes the limitations of traditional methods and achieves the goal of reducing labor intensity, saving time, and improving accuracy. It meets the actual needs of in-situ weld surface defect recognition for pipelines, pressure vessels, and other industrial complex products.

A CNN-LSTM and Attention-Mechanism-Based Resistance Spot Welding Quality Online Detection Method for Automotive Bodies

“…(2) Using deep learning, Dai et al [18] Overall, deep learning methods have become the main research trend in welding processes with real-time monitoring data. However, it can be seen that most of this research mainly focuses on vision detection from image signals [18][19][20][21]28]. Real-time data, such as welding current, dynamic resistance, and dynamic power, researched in the machine learning processes, have been considered less frequently in deep learning up to now.…”

“…Thus, it is necessary to Fortunately, along with the development of new generation information and artificial intelligence technology [5], data-driven welding quality online detection methods [6,7] have attracted the attention of researchers. The data used in this technology involve monitored dynamic resistance [8][9][10], electrode displacement [11,12], welding voltage [13,14], dynamic power [15,16] and welding spot image data [17][18][19][20][21]. Various machine learning and deep learning models have been developed and applied using these data.…”

Application of Machine Learning for Recognizing Surface Welding Defects in Video Sequences

“…Pattern recognition involves the tasks of detecting and classifying surface defects of products [1] occurring in industrial production, in particular, defects in fabric [2], marble slabs [3], steel plates [4], rolled steel sheets [5], and various steel products [6]. One of the research directions is the recognition of welding defects in metal products: [7], welding defects on an assembly line of fuel injectors [8], for pipelines and pressure vessels [9], on the surface of the engine transmission [10].…”

“…To solve the problem of recognizing welding defects, specifically to build classifiers, researchers use digital image sets obtained from a camera [7,9], digital radiographic images [11,12], and frames of video sequences. Researchers propose various solutions based on both traditional machine learning and deep learning methods to recognize welding defects in images.…”