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Robust visual semantic perception for flexible grinding of complex welds
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PRE
AI
DOI：10.1016/j.birob.2026.100288.png)
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Abstract
En 中文
Semantic segmentation methods based on RGB images exhibit notable limitations in complex industrial scenarios, particularly in addressing interference factors such as dynamic lighting variations and polymorphic weld seam morphologies, which lead to insufficient feature extraction capabilities and reduced segmentation accuracy and robustness. To address these limitations, this study proposes a polymorphic weld seam semantic segmentation model (PWSM) based on multi-level feature fusion, which effectively integrates the informational advantages of RGB and depth images to enhance perceptual capabilities in complex environments. The proposed model introduces a Dual-Stream Dual-modal Fusion (DSDF) module that employs channel selection and spatial selection strategies to extract and enhance complementary features from RGB and depth images. Concurrently, a Multi-Level Feature Fusion Module (ML-FFM) is developed to progressively integrate low-level and high-level semantic information through a multi-scale mechanism, refining boundary features while preserving the integrity of feature representation. Experimental results demonstrate that the model achieves superior segmentation performance on a complex multi-form weld seam dataset, particularly showing enhanced accuracy and robustness in challenging scenarios involving occlusions and illumination variations. Compared with existing single-modal and multi-modal models, the proposed model achieves performance improvements of 1.52% and 0.65%, respectively, providing effective technical support for intelligent perception of polymorphic weld seams. (c) 2026 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Keywords:
Visual semantic perception
Robustness
Adaptive
Polymorphic weld seams
Flexible grinding robot
Journal
B
IF:
5.4
Papers:
28
Citations:
0