To solve the problem of low resolution of infrared target images, lack of texture details, and low detection accuracy caused by complex background interference, an infrared target detection algorithm based on improved YOLOX is proposed. First, an effective spatial channel mixed attention module is introduced into the feature extraction backbone network CSP-Darknet53 to reduce the accuracy loss of the network due to long-distance transmission; secondly, in order to further improve the detection accuracy of infrared targets, based on the original enhanced feature extraction network PANet, an improved path feature fusion method is proposed; finally, in order to solve the problem of low recognition rate of small objects in infrared targets, a deconvolution operation is performed at the YOLOX output detection-head to expand the output feature map. Experiments are carried out on the FLIR infrared public data set. The experimental results show that the mean Average Precision (mAP) of the proposed algorithm recognition reaches 91.00%, which is 5.04% percentage points higher than that of the benchmark YOLOX network, it is effective to improve the detection accuracy of infrared targets.