Industrial Fusion Cascade Detection of Solder Joint

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 multitemplate 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.

Research on optical fiber magnetic field sensors based on multi-mode fiber and spherical structure

A magnetic field sensor with a magnetic fluid(MF)-coated intermodal interferometer is proposed and experimentally demonstrated. The interferometer is formed by sandwiching a segment of single mode fiber(SMF) between a segment of multi-mode fiber(MMF) and a spherical structure. It can be considered as a cascade of the traditional SMF-MMF-SMF structure and MMF-SMF-sphere structure. The transmission spectral characteristics change with the variation of applied magnetic field. The experimental results exhibit that the magnetic field sensitivities for wavelength and transmission loss are 0.047 nm/m T and 0.215 d B/m T for the interference dip around 1 535.36 nm. For the interference dip around 1548.41 nm,the sensitivities are 0.077 nm/m T and 0.243 d B/m T. Simultaneous measurement can be realized according to the different spectral responses.