An Integrated and Robust Vision System for Internal and External Thread Defect Detection with Adversarial Defense

Abstract

In industrial automation, detecting defects in threaded components is challenging due to their complex geometry and the concealment of micro-flaws. This paper presents an integrated vision system capable of inspecting both internal and external threads with high robustness. A unified imaging platform ensures synchronized capture of thread surfaces, while advanced image enhancement techniques improve clarity under motion blur and low-light conditions. To overcome limited defect samples, we introduce a generative data augmentation strategy that diversifies training data. For detection, a lightweight and optimized deep learning model achieves higher precision and efficiency compared with existing YOLO variants. Moreover, we design a dual-defense mechanism that effectively mitigates stealthy adversarial perturbations, such as alpha channel attacks, preserving system reliability. Experimental results demonstrate that the proposed framework delivers accurate, secure, and efficient thread defect detection, offering a practical pathway toward reliable industrial vision systems.