Traditional methods are unable to obtain ideal infrared weak vehicle target detection results, resulting in large detection errors that cannot meet practical application requirements. In order to address the limitations of tradi- tional infrared weak vehicle target detection methods, timely detect weak vehicles in infrared images, and improve ve- hicle detection accuracy, a convolutional neural network-based infrared weak vehicle target detection method was de- signed. Firstly, the infrared images required for weak and small vehicle target detection are collected, and the noise in the infrared images is processed to eliminate the interference of noise on weak and small vehicle target detection. Then, convolutional neural network is used to establish a weak and small vehicle target detection model. Finally, the performance of the weak and small vehicle target detection method in this paper is tested through specific simulation ex- periments. The results show that the detection accuracy of weak and small vehicle targets using this method exceeds 90%, significantly reducing the false detection rate of weak and small vehicle targets. At the same time, the detection time of weak and small vehicle targets is controlled within 5 seconds, which can meet the real-time requirements of weak and small vehicle target detection and has high practical application value.