To address the problems of poor textural detail, low contrast, and poor target detection in infrared images, a multiscale infrared target detection model that integrates a channel attention mechanism is proposed based on Yolov4 (You Only Look Once version 4). First, the number of model parameters is reduced by reducing the depth of the backbone feature extraction network. Second, to supplement the shallow high-resolution feature information, the multiscale feature fusion module is reconstructed to improve the utilization of the feature information. Finally, before the multiscale feature map is generated, the channel attention mechanism is integrated to further improve the infrared feature extraction ability and reduce noise interference. The experimental results show that the size of the algorithm model in this study was only 28.87% of the Yolov4. The detection accuracy of the infrared targets also significantly improved.