Abstract
As the main component of the new energy battery, the safety vent usually is welded on the battery plate, which can prevent unpredictable explosion accidents caused by the increasing internal pressure of the battery. The welding quality of safety vent directly affects the safety and stability of the battery; so, the welding-defect detection is of great significance. In this paper, we researched the welding-defect detection method based on semantic segmentation algorithm. The automatic detection method should recognize, locate, and count the area of defects. However, small differences between inter-classes, strong variations in defect size, and uncertain annotation create challenges for defect semantic segmentation. To address these challenges, a customized deep learning model based on two-branch architecture is proposed to segment welding-defect at the pixel level. This architecture involves three modules as follows, (i) The Spatial Branch is elaborately designed to capture spatial details and generate high-resolution feature representation; (ii) The Context Branch is responsible for obtaining semantic context; (iii) The Feature Fusion Block is proposed to merge the two types of feature representation and enhance mutual connections. Furthermore, a welding image dataset was built to validate our method, and the network’s performance of defect segmentation and classification were evaluated by the defects images and the normal images, respectively. Data augmentation strategies are performed to overcome the problem of imbalanced distribution and inaccurate labels of the dataset, which is caused by the ambiguity of defect edges and the subjectivity of manual annotation. The experiment results indicate that our method achieves 86.704\(\%\) of mIoU (mean intersection of union), which supports the effectiveness of the deep learning model in segmenting defects. Besides, 98.896\(\%\) of AP (average precision) and 0\(\%\) of MDR (miss detection rate) further suggests the applicability of the proposed framework in industrial applications.
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Acknowledgements
This research was partially supported by the Shenzhen Science and Technology Program under Grant JCYJ20210324093806017, and the Shenzhen-Hong Kong Joint Innovation Foundation under Grant SGDX20190919094401725.
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YY: Conceptualization, Methodology. YH: Data Curation, Writing—Original Draft, Software. HG: Resources.ZC: Investigation and Validation. ZC: Investigation and Validation. LZh: Supervision, Writing—Review and Editing.
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Yang, Y., He, Y., Guo, H. et al. Semantic segmentation supervised deep-learning algorithm for welding-defect detection of new energy batteries. Neural Comput & Applic 34, 19471–19484 (2022). https://doi.org/10.1007/s00521-022-07474-0
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DOI: https://doi.org/10.1007/s00521-022-07474-0