SLA-YOLO—Enhancing YOLO for Tiny Defect Detection in Industrial Defect Scenes

In recent years, the YOLO series has emerged as a widely adopted framework for real-time object detection because of its favorable balance between detection accuracy and inference efficiency. Nevertheless, accurate recognition and localization of tiny defects in industrial inspection remain challenging. These challenges mainly arise from the extremely small scale of defect targets, low image contrast, and the limited capability of conventional models in feature representation under uniform backgrounds. To address these issues from a mathematically optimized perspective and via feature modeling optimization, we develop a dedicated framework for tiny defect detection, termed SLA-YOLO. The main contributions of this work are as follows. First, we adopt a slicing-based processing strategy inspired by the SAHI framework, referred to as Image Slicing Processing (ISP) in this work, and extend it to both training and inference stages. This design enhances the relative scale of tiny defects within local regions, improving detection sensitivity and data diversity without introducing additional model complexity. Second, we introduce a Large Receptive-Field Selective Context (LRSC) module. By leveraging large-receptive-field selective convolution kernels, this module adaptively captures contextual information around critical defect regions via feature modeling optimization of scale-dependent representations. Third, we incorporate a Transformer-based High-level Feature Enhancement (THFE) module to improve global dependency modeling in high-level semantic representations, thereby enhancing feature discriminability for complex defect patterns. Experimental results on the CCB defect dataset show that SLA-YOLO improves mAP@50:95 by 2.7% and mAP@50 by 3.3%. In addition, the proposed method demonstrates strong generalization capability on other tiny object detection tasks.