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Wearing seat belts correctly is an important measure to prevent electrical workers from falling from heights. In order to solve the problem of low efficiency and poor effectiveness of whether workers wear seat belts in power sites, a safety belt detection method for power workers based on improved YOLOv8 was proposed. The algorithm adds SE attention mechanism to the network to improve the recognition ability of the model. At the same time, a weighted bidirectional feature pyramid network structure is introduced to perform feature fusion, improve feature learning ability, and reduce the complexity of the model. The WIoUv3 loss function is used to replace the original CIoU loss function, which further improves the detection accuracy and adaptability of the model to different environments. Experimental results show that the average accuracy of the proposed algorithm reaches 96.5%, and the recognition effect is significantly improved, which is better than other classical object detection models, which verifies the effectiveness of the new algorithm.