Steel defect detection is critical for industrial quality control, yet performance is constrained by multi-scale variations, small targets, and background interference. To enhance the accuracy and efficiency of the detection model, this paper proposes a defect detection network based on an improved version of YOLO11, named LiteSteel-YOLO. First, a Lightweight Multi-Scale Fusion module (C3k2-LMSF) is designed to enhance multi-scale defect perception through fused convolutional kernels and feature guidance mechanisms. Second, a spatial-channel aware upsampling module (SCAM) is proposed, which improves the robustness of small target detection and suppresses noise through channel reorganization and spatial offset operations. Finally, an Efficient-Head detector optimized via structural reconfiguration is introduced to maximize computational efficiency. Experimental results show that the LiteSteel-YOLO receives mAP@50 of 81.7% and 70.7% with inference speed of 338 and 530 FPS on the NEU-DET and GC10-DET datasets (surpassing YOLO11 by 4.0% and 2.3%). The proposed framework enhances the accuracy and efficiency of steel defect detection, providing a solution for industrial inspection scenarios.