Abstract: Aiming at the detection difficulties of infrared images such as low signal-to-noise ratio, poor resolution, and much noise and clutter. We propose a lightweight infrared image target detection algorithm ITD-YOLO based on YOLOv7. Firstly, the ITD-YOLO algorithm redesigns the network structure, and re-adjusts the architecture of the feature extraction network and the feature fusion network. Crop out the large receptive fields corresponding to the deep layers in the original network, and adjust the model preset anchor frames based on the output of the reconstructed network feature map. The relationship between deep and shallow features in multi-scale feature fusion is changed to increase the weight of the detail information extracted by the shallow network in the fusion to improve the detection performance of smaller targets; then, PConv is introduced into the ELAN module to replace the conventional convolution to further reduce the model computation. Next, the model loss function is adjusted to PolyLoss to accelerate the model convergence and further enhance the detection performance for targets; finally, SIoU is used as the edge loss function to enhance the localisation accuracy for targets. The experimental results show that ITB-YOLO can effectively improve the detection effect, and the mean average accuracy is increased by 2.27% and 7.29% compared with YOLOv7s on FLIR and OSU datasets, respectively. The volume of the model obtained after the improvement is only 17.7 MB, and the computation volume decreases by 37.11%. Comparing with the mainstream algorithms, ITD-YOLO has been improved to a certain extent in all the indexes, and can meet the real-time infrared target detection task.