Helmets and work clothes are important guarantees for the safety of substation staff. In order to solve the problem of low detection accuracy of existing detection models, this paper proposes a substation staff wear detection algorithm based on multi-branch deep convolution and dual attention. The algorithm proposes a multi-branch deep convolution (MBDC) network to add a deep separable convolution layer to enhance the completeness of feature extraction. Then, it is proposed that multimodal interaction attention (MIA) can increase the detection ability of the model to small targets, and combine the MIA mechanism with efficient channel attention (ECA) mechanism to form a dual attention mechanism to enhance the recognition accuracy of the model for small targets and occluded targets. Finally, the focus loss function and SIOU ( scylla intersection over union) are introduced as the loss function to solve the problem of positive and negative sample imbalance and accelerate the convergence speed. The experiment shows that the average accuracy of the algorithm in this paper is 84. 88%, 9. 92% higher than the original algorithm, and the overall performance is better than the comparison algorithm.