Based on the YOLOv8n model, this paper proposes a improved hazard detection model CML-YOLO for power tower components. It aims to solve the problems of low accuracy, large number of parameters, high computational complexity and large model weight of multi-scale power tower components hazard detection model under complex background. It is mainly used for the detection of targets such as damaged insulators, rusted dampers and bird nests. Firstly, the C2f-HEFE module is designed to enhance the ability to distinguish between background and target by enhancing the edge information. Secondly, the MSFFPN module is designed, and the multi-scale feature fusion is used to enhance the adaptability of the model to multi-scale targets. Finally, the lightweight LSBDH module is designed to reduce the number of parameters and calculation amount of the model. Experimental results show that compared with the baseline model YOLOv8n, the mean average precision of CML-YOLO is improved by 4.4%, and the number of parameters, calculation amount and model weight are reduced by 33.9%, 20.9% and 26.4% respectively. This model improves detection performance while maintaining its lightweight characteristics, achieving a good balance between model detection accuracy and model weight.