The accurate detection of computer room personnel can bring great convenience to computer room management and computer room inspection. Swin Transformer is used in object detection and achieves excellent detection performance. In this paper, Swin Transformer is used as the baseline to achieve accurate detection of computer room personnel. This paper mainly makes the following two contributions:1) In this paper, a practical self-attention method is designed. The channel interaction module is used in the self-attention calculation to solve the problem of local window self-attention lacking orientation awareness and location information. Reduce the size of input tokens through depth-wise convolution to reduce the complexity of self-attention calculation. 2) Use a balanced L1 loss and configure the weights of different stages of loss in the total loss function to solve the problem of imbalance between simple samples and difficult samples. Compared with the original Swin Transformer, the improved method improves the detection accuracy of mAP@0.5 by 3.2%.