GLF-NET: Global and Local Dynamic Feature Fusion Network for Real-Time Steel Strip Surface Defect Detection

Surface defect detection plays a crucial role in ensuring the quality standards of hot-rolled steel strips. To meet the demands for high precision and real-time performance in industrial defect detection, this paper introduces an improved one-stage detector based on YOLOv5s, named GLF-NET, that focuses on a good balance between speed and precision. Firstly, an Attention Augmented Module (AAM) is proposed and used in the backbone, with the aim of minimizing the loss of semantic and location information of defects during the process of feature extraction. Secondly, to enrich the model's capacity of representing multi-scale features of defects, an innovative Global and Local Dynamic Feature Fusion (GLF) module is designed and plugged into the top-down FPN part of the neck, bridging the semantic gap between different feature layers and enabling the model to adaptively select features for fusion. Additionally, a novel Receptive Field Augmented Module (RFA) is proposed and integrated into the bottom-up PAN structure of the neck, enhancing the detector's ability of perceiving defects with irregular shapes and large aspect ratios. Extensive experimental results on the NEU-DET steel strip surface defect dataset demonstrate that GLF-NET obtains an impressive mAP value of 79.2%, exceeding YOLOv5s by 4.2%. Furthermore, with an impressive detection speed of 95 Frames Per Second (FPS), GLF-NET not only meets the real-time demands of industrial defect detection but also demonstrates exceptional capabilities in defect detection. Code is available at https://github.com/MYF1124/GLF-NET.