Open Access

ARTICLE

Industrial Fusion Cascade Detection of Solder Joint

Chunyuan Li^1,2,3, Peng Zhang^1,2,3, Shuangming Wang⁴, Lie Liu⁴, Mingquan Shi^2,*

1 Chongqing School, University of Chinese Academy of Sciences, Chongqing University, Chongqing, 400044, China
2 Automatic Reasoning and Cognition Research Center, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, 400714, China
3 College of Artificial Intelligence, Chongqing School, University of Chinese Academy of Sciences, Chongqing, 400714, China
4 Design Department, Liuzhou Fuzhen Bodywork Industrial Co., Ltd., Liuzhou, 545006, China

* Corresponding Author: Mingquan Shi. Email:

Computers, Materials & Continua 2024, 81(1), 1197-1214. https://doi.org/10.32604/cmc.2024.055893

Received 09 July 2024; Accepted 09 September 2024; Issue published 15 October 2024

Abstract

With the remarkable advancements in machine vision research and its ever-expanding applications, scholars have increasingly focused on harnessing various vision methodologies within the industrial realm. Specifically, detecting vehicle floor welding points poses unique challenges, including high operational costs and limited portability in practical settings. To address these challenges, this paper innovatively integrates template matching and the Faster RCNN algorithm, presenting an industrial fusion cascaded solder joint detection algorithm that seamlessly blends template matching with deep learning techniques. This algorithm meticulously weights and fuses the optimized features of both methodologies, enhancing the overall detection capabilities. Furthermore, it introduces an optimized multi-scale and multi-template matching approach, leveraging a diverse array of templates and image pyramid algorithms to bolster the accuracy and resilience of object detection. By integrating deep learning algorithms with this multi-scale and multi-template matching strategy, the cascaded target matching algorithm effectively accurately identifies solder joint types and positions. A comprehensive welding point dataset, labeled by experts specifically for vehicle detection, was constructed based on images from authentic industrial environments to validate the algorithm’s performance. Experiments demonstrate the algorithm’s compelling performance in industrial scenarios, outperforming the single-template matching algorithm by 21.3%, the multi-scale and multi-template matching algorithm by 3.4%, the Faster RCNN algorithm by 19.7%, and the YOLOv9 algorithm by 17.3% in terms of solder joint detection accuracy. This optimized algorithm exhibits remarkable robustness and portability, ideally suited for detecting solder joints across diverse vehicle workpieces. Notably, this study’s dataset and feature fusion approach can be a valuable resource for other algorithms seeking to enhance their solder joint detection capabilities. This work thus not only presents a novel and effective solution for industrial solder joint detection but lays the groundwork for future advancements in this critical area.

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