In view of the difficulty of snow leopard detection and recognition in infrared camera images

a snow leopard detection algorithm is proposed based on EfficientDet

which combines domain migration and new attention mechanism.Firstly

the algorithm adopts image enhancement to expand the training sample and adds non-snow leopard images to optimize the dataset structure to improve the robustness of the model.Secondly

the Generative Adversarial Networks is used to transfer the domain of night infrared gray image

and the image prediction results before and after migration are fused to solve the problem of weak target recognition ability in infrared image.Finally

the attention mechanism is added to the backbone feature extraction network to refine the feature map to improve the visual significance of snow leopard in RGB and infrared images

and the improved feature fusion network structure to integrate more effective information.The results of the contrast experiment and the ablation experiment show that the proposed method has better detection speed and accuracy than Faster-RCNN，YOLOv3 and SSD. The improved algorithm has an average accuracy of 97.4% and detection speed of 19 images/s.This detection model is more suitable for locating and recognizing snow leopard RGB and infrared images.