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arXiv · 2311.10952

NAS-ASDet: An Adaptive Design Method for Surface Defect Detection Network using Neural Architecture Search

Zhenrong Wang·Bin Li·Weifeng Li·Shuanlong Niu·Wang Miao·Tongzhi Niu
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Abstract

Deep convolutional neural networks (CNNs) have been widely used in surface defect detection. However, no CNN architecture is suitable for all detection tasks and designing effective task-specific requires considerable effort. The neural architecture search (NAS) technology makes it possible to automatically generate adaptive data-driven networks. Here, we propose a new method called NAS-ASDet to adaptively design network for surface defect detection. First, a refined and industry-appropriate search space that can adaptively adjust the feature distribution is designed, which consists of repeatedly stacked basic novel cells with searchable attention operations. Then, a progressive search strategy with a deep supervision mechanism is used to explore the search space faster and better. This method can design high-performance and lightweight defect detection networks with data scarcity in industrial scenarios. The experimental results on four datasets demonstrate that the proposed method achieves superior performance and a relatively lighter model size compared to other competitive methods, including both manual and NAS-based approaches.

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Zhenrong Wang, Bin Li, Weifeng Li, Shuanlong Niu, Wang Miao, Tongzhi Niu. 2023-11-18. NAS-ASDet: An Adaptive Design Method for Surface Defect Detection Network using Neural Architecture Search. https://arxiv.org/abs/2311.10952

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