Abstract: According to the characteristics of infrared targets, an anchor-free lightweight infrared target detection method was proposed, which improved the detection ability of embedded platform. For the platform with limited computing resources, a new lightweight convolution structure was proposed. Asymmetric convolution was introduced to enhance the feature expression ability of standard convolution, reducing the amount of parameters and computation effectively. A lightweight feature extraction unit was constructed by designing parallel multi-feature path, which generated rich features through channel concatation, then combining with attention module and channel shuffle. SkipBranch was added to promote the transmission of shallow information to the high level and further enrich the characteristics of the high level. Experiments on FLIR dataset showed that the accuracy of the designed lightweight network structure was 81.7%, which exceeded YOLOv4-tiny. However, the model parameters and calculation amount were reduced by 75.0% and 71.1% respectively, and the reasoning time was compressed by 91.3%, which could meet the real-time detection requirements of infrared object on embedded platform.