To address the problem of small targets being difficult to identify during strip defect detection and the large number of target detection algorithm parameters affecting model deployment, this paper proposes a lightweight strip defect detection algorithm based on the YOLOv11 framework. The algorithm enhances small target recognition capability through the integration of a bidirectional feature pyramid network (BiFPN) and GAM attention mechanism. Simultaneously, a lightweight SlimNeck model is adopted to integrate the neck network to reduce model parameters and complexity while maintaining detection accuracy. Experimental validation on the NEUDET dataset demonstrates that the improved BSG-LiteYOLO detection model based on YOLOv11n significantly outperforms the original YOLOv11n. The optimized model achieves a 25.6% reduction in parameters, 22.6% decrease in model weight size, 7.94% reduction in floating point operations (FLOPs), while improving mAP@0.5 by 4.19%. Experimental results demonstrate the feasibility of the improved model for steel strip surface defect detection, with the optimized algorithm successfully deployed on Jetson Orin Nx edge computing devices achieving 34.3 FPS, which meets practical production requirements.