An adaptive anchor neural network for defect detection in aluminum profiles images

Liangjie Dong; Huan Wang; Bo Xu; Yaoyue Zheng; Yarong Shi; Tao Xu; Zhiqiang Tian

doi:10.1117/12.2604761

28 October 2021 An adaptive anchor neural network for defect detection in aluminum profiles images

Liangjie Dong, Huan Wang, Bo Xu, Yaoyue Zheng, Yarong Shi, Tao Xu, Zhiqiang Tian

Proceedings Volume 11884, International Symposium on Artificial Intelligence and Robotics 2021; 118840X (2021) https://doi.org/10.1117/12.2604761
Event: International Symposium on Artificial Intelligence and Robotics 2021, 2021, Fukuoka, Japan

Abstract

The quality of Aluminum Profiles is the most important evaluation criterion in industrial production. To perform the quality control of Aluminum Profiles, strict defect detection must be carried out. Traditional machine learning methods need to design hand-crafted features in advance. Deep learning methods need to preset anchor parameters according to all defects, which is inefficient and inaccurate. In this paper, we propose an adaptive anchor network with an attention-based refinement mechanism for defect detection. The network has learnable parameters to generate anchors adaptively. Meanwhile, to better represent the different defects, we design a refinement module with the channel and spatial attention mechanism and deformable convolution at the stage of feature extraction. Besides, we also use cascade detection architecture to retain more defect information. The proposed method gets the AP of 62.4 and AP50 of 86.1 on an industrial dataset, which has AP of 12.8 and AP₅₀ of 17.8 improved to the conventional methods and outperforms several state-of-the-art methods.

Citation Download Citation

Liangjie Dong, Huan Wang, Bo Xu, Yaoyue Zheng, Yarong Shi, Tao Xu, and Zhiqiang Tian "An adaptive anchor neural network for defect detection in aluminum profiles images", Proc. SPIE 11884, International Symposium on Artificial Intelligence and Robotics 2021, 118840X (28 October 2021); https://doi.org/10.1117/12.2604761

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KEYWORDS

Aluminum

Convolution

Defect detection

Avalanche photodetectors

Autoregressive models

Neural networks

Feature extraction

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