Attention-based convolution neural network for magnetic tile surface defect classification and detection

Abstract

Effectively identifying surface defects in magnetic tiles has proven to be highly challenging due to limited sample availability and irrelevant background interference, which also plays a crucial role in significantly influencing the lifespan and reliability of permanent magnet motors. To address these challenges, our study draws inspiration from a comprehensive analysis of the retinal attention mechanism and proposes three guiding criteria: multi-level resolution, what to look for, and where to look at. These criteria are utilized as foundational principles to enhance the representation learning capability of designed neural network structures through the incorporation of the retinal attention mechanism. Subsequently, based on these guiding criteria, we introduce a novel convolutional retinal attention block (CRAB) to learn discriminative and robust feature representations for magnetic tile surface defect classification and detection. The proposed CRAB comprises three modules: multi-resolution module (MRM), global attention aggregation module (GAAM), and local attention aggregation module (LAAM), designed to extract discriminative and robust features by refining meaningful information and suppressing redundant ones. Comprehensive experimental results across image classification and object detection tasks demonstrate that the proposed CRAB outperforms existing methods such as SE, ECA, and CBAM, and can effectively amplify the representation power across various backbone networks, including VGG-16, GoogLeNet, ResNet-18, and ResNet-50. An evaluation on surface defect classification and detection tasks for industrial magnetic tiles further shows that CRAB achieves accuracies of 99.50% and 96.98%, respectively. These results emphasize the promising application prospects of the proposed method in detecting industrial surface defects amid expansive and inconsequential backgrounds. The code of the proposed method is available at: https://github.com/KWflyer/CRAB.