A vehicle-based thermal imaging system does not depend on a light source, is insensitive to weather, and has a long detection distance. Automatic target detection using vehicle-based thermal imaging is of great significance for intelligent night driving. Compared with visible images, the infrared images acquired by a vehicle-based thermal imaging system based on existing algorithms have low resolution, and the details of small long-range targets are blurred. Moreover, the real-time algorithm performance required to address the vehicle speed and computing ability of the vehicle-embedded platform should be considered in the vehicle-based thermal imaging target detection method. To solve these problems, an enhanced lightweight infrared target detection network (I-YOLO) for a vehicle-based thermal imaging system is proposed in this study. The network uses a tiny you only look once(Tiny-YOLOV3) infrastructure to extract shallow convolution-layer features according to the characteristics of infrared images to improve the detection of small infrared targets. A single-channel convolutional core was used to reduce the amount of computation. A detection method based on a CenterNet structure is used to reduce the false detection rate and improve the detection speed. The actual test shows that the average detection rate of the I-YOLO target detection network in vehicle-based thermal imaging target detection reached 91%, while the average detection speed was81 fps, and the weight of the training model was96MB, which is suitable for deployment on a vehicle-based embedded system.