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Article

SABE-YOLO: Structure-Aware and Boundary-Enhanced YOLO for Weld Seam Instance Segmentation

1
Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China
2
School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China
3
School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China
*
Authors to whom correspondence should be addressed.
J. Imaging 2025, 11(8), 262; https://doi.org/10.3390/jimaging11080262
Submission received: 27 June 2025 / Revised: 30 July 2025 / Accepted: 5 August 2025 / Published: 6 August 2025
(This article belongs to the Section Image and Video Processing)

Abstract

Accurate weld seam recognition is essential in automated welding systems, as it directly affects path planning and welding quality. With the rapid advancement of industrial vision, weld seam instance segmentation has emerged as a prominent research focus in both academia and industry. However, existing approaches still face significant challenges in boundary perception and structural representation. Due to the inherently elongated shapes, complex geometries, and blurred edges of weld seams, current segmentation models often struggle to maintain high accuracy in practical applications. To address this issue, a novel structure-aware and boundary-enhanced YOLO (SABE-YOLO) is proposed for weld seam instance segmentation. First, a Structure-Aware Fusion Module (SAFM) is designed to enhance structural feature representation through strip pooling attention and element-wise multiplicative fusion, targeting the difficulty in extracting elongated and complex features. Second, a C2f-based Boundary-Enhanced Aggregation Module (C2f-BEAM) is constructed to improve edge feature sensitivity by integrating multi-scale boundary detail extraction, feature aggregation, and attention mechanisms. Finally, the inner minimum point distance-based intersection over union (Inner-MPDIoU) is introduced to improve localization accuracy for weld seam regions. Experimental results on the self-built weld seam image dataset show that SABE-YOLO outperforms YOLOv8n-Seg by 3 percentage points in the AP(50–95) metric, reaching 46.3%. Meanwhile, it maintains a low computational cost (18.3 GFLOPs) and a small number of parameters (6.6M), while achieving an inference speed of 127 FPS, demonstrating a favorable trade-off between segmentation accuracy and computational efficiency. The proposed method provides an effective solution for high-precision visual perception of complex weld seam structures and demonstrates strong potential for industrial application.
Keywords: weld seam recognition; deep learning; instance segmentation; YOLO weld seam recognition; deep learning; instance segmentation; YOLO

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MDPI and ACS Style

Wen, R.; Xie, W.; Fan, Y.; Shen, L. SABE-YOLO: Structure-Aware and Boundary-Enhanced YOLO for Weld Seam Instance Segmentation. J. Imaging 2025, 11, 262. https://doi.org/10.3390/jimaging11080262

AMA Style

Wen R, Xie W, Fan Y, Shen L. SABE-YOLO: Structure-Aware and Boundary-Enhanced YOLO for Weld Seam Instance Segmentation. Journal of Imaging. 2025; 11(8):262. https://doi.org/10.3390/jimaging11080262

Chicago/Turabian Style

Wen, Rui, Wu Xie, Yong Fan, and Lanlan Shen. 2025. "SABE-YOLO: Structure-Aware and Boundary-Enhanced YOLO for Weld Seam Instance Segmentation" Journal of Imaging 11, no. 8: 262. https://doi.org/10.3390/jimaging11080262

APA Style

Wen, R., Xie, W., Fan, Y., & Shen, L. (2025). SABE-YOLO: Structure-Aware and Boundary-Enhanced YOLO for Weld Seam Instance Segmentation. Journal of Imaging, 11(8), 262. https://doi.org/10.3390/jimaging11080262

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