Aiming at the problem of target misdetection and missed detection in infrared images under complex street backgrounds due to factors such as occlusion and lack of texture details, this paper proposes an infrared target detection algorithm for complex street scenes. Using YOLOv8n as the baseline model, firstly, a multi branch convolutional structure is designed to enhance feature extraction and expression. Structural reparameterization is used to decouple the training and inference stages, improve the inference speed of the model, and global self attention estimation is introduced to accelerate the calculation of attention. The time complexity is reduced to O(n), enabling the convolutional kernel attention to achieve dynamic identity. Secondly, combining the advantages of depthwise separable convolution and deformable convolution, after feature fusion between the upsampling results and the output features of the backbone network, a salient information aware deformable convolution attention gating mechanism is introduced to improve the semantic information richness of the fused features. Finally, An efficient intersection and union ratio replace the localization loss function, calculate the length and width influence factors of the predicted box and the true box separately, and accelerate the convergence speed. Validation experiments were conducted on the Flir dataset, and the average accuracy of the improved algorithm reached 79.5%, which is 3.9% higher than the YOLOv8n algorithm. This validates the superiority of the proposed algorithm in infrared target detection under complex street backgrounds.