Surface Defect Detection: A comparative analysis of Deep Learning-based Frameworks

Automated detection and localization of surface defects is critical for the timely maintenance and repair of planar materials-based industrial products from the automobile to the aerospace industry. This paper conduct a detailed and systematic comparative analysis of various anchor-based and anchor-free DL-based algorithms. The experimental results are further analyzed using the mean Average Precision (mAP) value and the impact to which augmentation strategies generalizes the model performance. The comparative analysis study presented in this paper helps to gain insight into the strengths and limitations of the popular DL-based frameworks under practical constraints with their real-time deployment feasibility.