Abstract: Steel is prone to surface defects due to processing and environmental factors in modern industrial production, which adversely affect its overall quality and service life. To overcome the shortcomings of existing detection methods, such as, inadequate accuracy and high computational complexity, this paper proposes an improved YOLOv8-based algorithm, GSL-YOLO. To address the issues of feature redundancy and insufficient feature expression ability, Ghost Bottleneck is introduced in the C2f module to replace the standard Darknet Bottleneck, thereby reducing redundant computation and enhancing feature extraction efficiency. DynamicConv is used to replace the standard CBS module to enhance adaptability to complex scenarios. To further improve the detection accuracy for minute defects, the SENetV1 attention mechanism is integrated into the neck network, which enhances the focus on key features by adaptively adjusting channel weights. In addition, a lightweight shared convolution detection head named LSDECD is designed to address the issues of large number of detection head parameters and high computational complexity, effectively reducing the number of model parameters and the cost of computation. The experimental results demonstrate that, on the NEU-DET steel surface defect dataset, GSL-YOLO achieves a 3% increase in mAP50 compared with YOLOv8, while he parameter quantity is reduced by 33% and the computational cost is reduced by 37% simultaneously. The proposed method achieves good lightweight design while improving the accuracy of model detection, meeting the application requirements of industrial real-time detection.