Smartphone screen surface defect detection using dynamic large separable kernel attention and multi-scale feature bi-directional path aggregation network.

In the smartphone manufacturing industry, detecting cover glass defects is crucial to product quality. To address this, this paper proposes DY-YOLO, an enhanced YOLOv8-based model for defect detection on smartphone cover glass. The model improves the accuracy and efficiency of detecting defects on cover glass surfaces in complex production environments. Specifically, the proposed Dynamic-Large Separable Kernel Attention (Dynamic-LSKA) module effectively suppresses interference from complex backgrounds, such as glass reflections, thereby reducing false detections. DY-YOLO integrates several innovations: the Dynamic-LSKA module for enhanced multi-scale perception, the Dynamic-C2f module for enhanced feature extraction, and the Advanced Screening Feature Bidirectional Path Aggregation Network (HSF-BPAN) for efficient fusion of advanced screening features. Additionally, DySample is used as a lightweight dynamic up-sampler to reduce computational cost. Extensive evaluations were conducted using two public benchmarks, Mobile Phone Screen Surface Defect Dataset (MSD) and Smartphone Screen Glass Dataset (SSGD). Results demonstrate that, compared to the baseline model, the proposed method achieves improvements of 1% and 0.6% in mAP@0.5 and mAP@0.5:0.95, respectively, on MSD, reaching 99.3% and 70.9%. On SSGD, the improvements are 4.8% and 2.6%, reaching 46% and 20.2%, respectively, surpassing the state-of-the-art methods in detection accuracy. Moreover, DY-YOLO achieves an excellent balance between performance and efficiency. With a parameter count comparable to the baseline but 33.3% lower computational cost, the model achieves an inference speed of 121.8 FPS, demonstrating its strong potential for real-time edge deployment on production lines. These results confirm the model's effectiveness and potential for industrial applications.