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

• URL: http://arxiv.org/abs/2311.10952v1
• Date: Sat, 18 Nov 2023 03:15:45 GMT
• Title: NAS-ASDet: An Adaptive Design Method for Surface Defect Detection Network using Neural Architecture Search
• Authors: Zhenrong Wang, Bin Li, Weifeng Li, Shuanlong Niu, Wang Miao, Tongzhi Niu
• Abstract summary: 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. Then, a progressive search strategy with a deep supervision mechanism is used to explore the search space faster and better.
• Score: 5.640706784987607
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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