TAANet: A Task-Aware Attention Network for Weak Surface Defect Detection

Coupling the classification and localization head compromises the defect detection performance due to feature conflict and spatial misalignment between these two subtasks. In this article, we propose a task-aware attention network (TAANet) for weak surface defect detection, referred to as TAANet. Specifically, we devise a feature-aware decoupled head (FADH) to learn task-specific features for defect classification and box regression, respectively. FADH is composed of two parallel branches, where one aims to encode a strong semantic context preferred for classification, and the other is to learn more structural information expected for regression, thus disentangling the two tasks at the feature level. Moreover, an efficient attention-guided feature pyramid network (AG-FPN) is developed by introducing attention guidance maps in the unidirectional FPN to enhance the feature representations of weak defects, replacing continuously stacked top-down and bottom-up connections. AG-FPN shortens the information transmission pathway in an efficient fashion and extracts more salient and discriminative features for the prediction layers in FADH. The proposed TAANet yields state-of-the-art 86.3% and 77.6% mAP@IoU = 0.5 at 58.7 FPS on the publicly available datasets, NEU-DET and GC10-DET, which effectively improves the defect detection performance over comparable detectors.