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

TL;DR

This paper introduces NAS-ASDet, an adaptive neural architecture search method that designs efficient, high-performance surface defect detection networks tailored for industrial scenarios with limited data.

Contribution

The paper presents a novel NAS-based approach with a specialized search space and progressive strategy for designing lightweight, effective defect detection networks.

Findings

Achieves superior detection accuracy on four datasets.

Produces models that are lighter and more efficient than existing methods.

Demonstrates effectiveness in data-scarce industrial environments.

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.