A novel efficient method for welding spots detection

Zhicheng He, Yadong Ma, Zhenxing Wang, Quan Bing Eric Li

Research output: Contribution to journalArticlepeer-review

Abstract

The traditional welding spots detection methods are very sensitive to light pollution, rust and oil stain. In this work, a novel method that combines light-weight deep learning model and circle detection algorithm for welding spots detection is proposed. First, a light-weight model called Modified You Only Look Once version 2 (M-YOLOv2) which substitutes MobileNetv2 for Darknet-19 convolution structure in YOLOv2 and incorporates fine-grained features (FGF) is developed. The proposed M-YOLOv2 is very effective and efficient to capture the position of welding spot compared with YOLOv2, YOLOv3 and Tiny-YOLOv2. Secondly, a novel right-triangle circle detection (RTCD) is developed to refine the shapes of the welding spots. The novel algorithm called M-YOLOv2-RTCD that combines M-YOLOv2 and RTCD is able to accurately and fast detect welding spots. The theoretical analysis and experimental results demonstrate that the proposed M-YOLOv2-RTCD outperforms the methods only using M-YOLOv2 or RTCD, in terms of accuracy and real-time performance.
Original languageEnglish
JournalMultimedia Tools and Applications
Publication statusAccepted/In press - 9 Mar 2022

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