SSGDD-YOLO: Multiscale Feature Fusion and Multi-Attention based YOLO for Smartphone Screen Glass Defect Detection

Surface defect detection is essential for ensuring the product quality of smartphone screen glass. In this work, a smartphone screen glass defect detection model based on an enhanced YOLOv7 framework with multiscale feature fusion and multi-attention, named SSGDD-YOLO is proposed. In the developed SSGDD-YOLO model, the branch fusion block (BFB) is integrated low-level features from multiple scales through parallel processing, to enhance the details in lower-level features for minimizing the information loss as less as possible. Furthermore, the SPPCSPC module of the head is improved as the SPPCSPC-I module, by replacing the standard max pooling with local importance-based pooling (LIP) that reflects the importance of features. The developed SPPCSPC-I module allows the network to automatically learn adaptive importance weights of features during downsampling, enhancing the multiscale feature extraction capability with diverse receptive fields. Finally, a contour-mixed attention block (C-MAB) is inserted into the feature fusion section of the network, which enhances spatial and channel information of features to reduce target information loss, improving the representation capability. Experiments are conducted using a challenging real-world defect image dataset gathered from a smartphone screen glass inspection line in an industrial plant. Results show the proposed SSGDD-YOLO model can achieve the highest mAP of 62.46% among all compared methods.