Multi-level supervised vision language model based steel surface defect detection

Ying Tan; Jing Shan; Jiaying Wang

doi:10.1117/12.3026387

9 February 2024 Multi-level supervised vision language model based steel surface defect detection

Ying Tan, Jing Shan, Jiaying Wang

Proceedings Volume 13073, Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023); 130730A (2024) https://doi.org/10.1117/12.3026387
Event: Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023), 2023, Changsha, China

Abstract

The shape of defects on steel surfaces is highly variable and training samples are limited, making it a significant challenge to transfer a high-performance pretrained vision language model to steel surface defect detection. Therefore, a Multi-level Supervised Vision Language Model based Steel Surface Defect Detection method MLS-VLM is proposed in this paper. MLS-VLM delves deeply into the extraction of profound features from limited samples with three levels of training: supervised contrast training from labeled areas and the entire image, as well as self-supervised contrast learning from Region Proposals. MLS-VLM can be rapidly transferred to two-stage object detector. Experimental results demonstrate that, compared to traditional object detection methods, MLS-VLM achieves 5.68~8.37 mAP improvement on three benchmark object detectors.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ying Tan, Jing Shan, and Jiaying Wang "Multi-level supervised vision language model based steel surface defect detection", Proc. SPIE 13073, Third International Conference on High Performance Computing and Communication Engineering (HPCCE 2023), 130730A (9 February 2024); https://doi.org/10.1117/12.3026387

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KEYWORDS

Education and training

Visual process modeling

Defect detection

Object detection

Image processing

Feature extraction

Data modeling

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