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