Defect Inspection using Gravitation Loss and Soft Labels

Defect inspection is a widely studied computer vision task with a vast range of applications. However, due to the great variety of defects and high difficulty of collecting ample abnormal images of rare occasions, such a task full of confusing noisy samples faces tremendous challenges of generalization. In this paper, we propose a practical framework for defect inspection to better discover and utilize the connections among these samples. Specifically, Gravitation Loss is proposed to enhance the discriminative power of embedding vectors learned by neural networks. Besides, Soft Loss is designed by introducing soft labels which provide more supervision for noisy samples to improve generalization. With joint supervision of Gravitation Loss, Soft Loss, and a regular classification loss, experimental results show that our method outperforms other state-of-the-art approaches on a newly-built resin plug-hole dataset.