To address the issues of limited target diversity, low detection accuracy, and poor generalization in the detection of safety protective equipment for aerial workers in electric power scenarios, this paper proposes an improved YOLO11n-based detection algorithm tailored for high-altitude safety protective equipment detection. Firstly, a DySample dynamic upsampling method is introduced into the neck network to effectively prevent excessive amplification or information loss during upsampling, thereby enhancing feature retention while maintaining overall detection performance. Secondly, the RCM is optimized using depthwise separable convolutions to construct a new CFSCM, which captures key features across both spatial and channel dimensions, improving the model′s perception of foreground protective equipment. Finally, a novel LQEH is designed to integrate the localization quality scores from the regression branch with the outputs of the classification branch, thereby addressing the lack of interaction between the two original branches and enhancing the correlation between classification and localization tasks. Experimental results demonstrate that the proposed algorithm achieves a mAP@0.5 of 93.1%, precision of 96.1%, and recall of 86.7%, representing improvements of 3.2%, 0.7% and 2.3% over the baseline model, respectively, with a detection speed of 131 fps. In addition, generalization experiments conducted on a high-altitude safety protective equipment dataset from the Roboflow platform show respective improvements of 2.1%, 5.2%, and 2.2% in mAP@0.5, precision, and recall compared to the baseline, validating the effectiveness of the proposed improvements in enhancing detection accuracy and generalization capability.