To address the issues of inadequate accuracy, complex model architecture, and poor generalization in detecting standardized work uniform within industrial scenarios using existing object detection networks, a novel high-precision lightweight model named SGAD-YOLO based on YOLO11 is proposed.First, the C3k2 module is improved by combining the StripBlock structure and CGLU mechanism. Through multi-level feature processing and dynamic feature enhancement, the model′s perception of slender features and complex textures is improved, while the model′s parameters and computational complexity are reduced. Second, the AFGCAttention mechanism is introduced to enhance the model′s focus on key regions and effectively suppress background noise interference through the dynamic fusion of global context information and local features. Finally, the Detect-SEAM detection head is redesigned to improve the model′s detection accuracy for occluded and small objects in complex environments. Experimental results demonstrate that the improved algorithm achieves mAP@0.5 values of 93.6% and 94.6% on the power grid field operation dataset and the public Roboflow 5 dataset, respectively—representing improvements of 1.5% and 2.1% over the baseline model. Moreover, its parameters and computational complexity are reduced by 8.3% and 7.4%, respectively. This proves that the SGAD-YOLO algorithm has better detection performance for standardized work uniform detection tasks in industrial scenarios.