Abstract: With the continuous development of high-speed aircraft, target detection and recognition, as a key part of precision guidance, requires higher real-time and high-accuracy target positioning and recognition. At present, the need for accurate detection and identification of time-sensitive targets such as armored vehicles and vehicle positions is increasingly urgent. Deep learning algorithms have advantages in feature extraction and classifier design. This paper takes the small-sized infrared vehicle target under a specific complex background as the research object, and develops a lightweight deep learning algorithm based on infrared dim vehicle target detection and recognition to meet the needs of less sample data, limited platform resources, high real-time requirements, and high detection accuracy. The project is light-weight cut based on the YOLOv5 algorithm, reduce the structure of the model and improve the real-time performance; a hybrid domain attention mechanism module EPA is proposed, which enables the algorithm to focus on important channels more quickly and effectively through a local cross-channel interaction strategy without dimensionality reduction. Suppressing invalid channels and combining the channel attention mechanism with the spatial attention mechanism makes the algorithm pay more attention to the pixel information related to the target. The Residual Dense Attention Module (RDAB) is proposed, which is composed of dense residual blocks and attention mechanism EPA. It extracts sufficient local features through dense convolutional layers, and obtains more effective channel and pixel information through attention mechanism, which can make the algorithm obtain better detection effect. Detect and identify the small-size infrared vehicle target data after data augmentation, and compare experiments with a variety of typical algorithms. It can be seen from the experimental results that the detection and recognition effect of the JH-YOLOv5-RDAB network proposed in this paper is better than other networks, and the weight size is only 6.6 MB, which is only half of the weight of the YOLOv5s algorithm model, but the algorithm detection effect is better, and the detection effect of the algorithm is close YOLOv5l whose weight size is 93.7 MB, with mAP50 reaching 95.1%. The experimental results show the superiority and feasibility of this network in infrared dim vehicle target detection.