In order to improve the detection and recognition of small targets and obscured targets in infrared images, the Efficientnetbased infrared target detection algorithm is proposed for the problem of low accuracy and low recall of infrared target detection in complex scenes. First, the efficient and lightweight Efficientnet is used as the feature extraction backbone of the model to reduce the number of parameters of the model and improve the training speed. In the last output layer of Efficientnet backbone, SPP module is introduced to enrich the expression capability of feature map, perform multiscale fusion and expand the perceptual field of feature map; in the feature fusion part of the model, FPN feature pyramid network is used, and CSPNet module and ECA attention mechanism are added after feature fusion to enhance feature extraction. The detection part uses YOLO Head to classify and regress the targets, and uses CIoU Loss as the bounding box regression loss function to improve the recognition ability of the obscured targets. The experimental results show that the Efficientnetbased model is only 188% of the size of YOLOv3, and the mAP reaches 8074% on the FLIR dataset, which is 1012% better than the YOLOv3 algorithm, and the model improves the detection accuracy while reducing the number of model parameters. The model has good generalization ability on the FLIR dataset and improves the detection of small and occluded targets.