YOLOv8-FCS: A more focused YOLOv8 model for defect detection in images of steel surface

Abstract

Defect detection in steel surface is crucial for engineering quality control. Traditional methods for detecting surface defects on steel materials have issues such as low detection accuracy, slow speed, low level of intelligence, and insufficient utilization of images. In response to these challenges, this paper proposes an improved YOLOv8 model for efficient and accurate detection of defects on steel surface. Firstly, we introduce a single-channel adversarial input strategy (AIS) to enhance the utilization of single-channel images and improve the network's detection effectiveness. Secondly, we utilize various attention modules to enhance the Neck and detection head of the network, thereby further improving the network's expressive power and detection performance. Finally, experiments were conducted on three open datasets, achieving a mAP (mean average precision) of 77.3% on the NEU-DET dataset, outperforming YOLOv8 at 74.1%, a mAP of 65.5% on the GC10 dataset, outperforming YOLOv8 at 64.0%, and a mAP of 73.8% on the Magnetic-tile-defect-datasets, outperforming YOLOv8 at 71.2%. Additionally, the average detection speed of this model is 93 frames per second, effectively balancing detection accuracy and efficiency.