Wen Rui, Xie Wu, Fan Yong, Shen Lanlan
Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin 541004, China.
School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin 541004, China.
J Imaging. 2025 Aug 6;11(8):262. doi: 10.3390/jimaging11080262.
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.
在自动化焊接系统中，精确的焊缝识别至关重要，因为它直接影响路径规划和焊接质量。随着工业视觉的快速发展，焊缝实例分割已成为学术界和工业界的一个突出研究重点。然而，现有方法在边界感知和结构表示方面仍面临重大挑战。由于焊缝固有的细长形状、复杂几何形状和模糊边缘，当前的分割模型在实际应用中往往难以保持高精度。为了解决这个问题，提出了一种用于焊缝实例分割的新型结构感知和边界增强YOLO（SABE-YOLO）。首先，设计了一个结构感知融合模块（SAFM），通过带状池化注意力和逐元素乘法融合来增强结构特征表示，以应对提取细长和复杂特征的困难。其次，构建了一个基于C2f的边界增强聚合模块（C2f-BEAM），通过集成多尺度边界细节提取、特征聚合和注意力机制来提高边缘特征敏感性。最后，引入基于内部最小点距离的交并比（Inner-MPDIoU）来提高焊缝区域的定位精度。在自建的焊缝图像数据集上的实验结果表明，SABE-YOLO在AP(50-95)指标上比YOLOv8n-Seg高出3个百分点，达到46.3%。同时，它保持了较低的计算成本（18.3 GFLOPs）和少量参数（6.6M），同时实现了127 FPS的推理速度，在分割精度和计算效率之间展现出良好的权衡。所提出的方法为复杂焊缝结构的高精度视觉感知提供了一种有效解决方案，并展示了强大的工业应用潜力。
