Wild animal object detection based on infrared camera images is conducive to the research and protection of wild animals. Because of the large difference in the number of different species of wildlife， there is the long-tail data problem of uneven distribution of numbers of species in the wildlife dataset collected by infrared cameras. This problem affects the overall performance improvement of the object detection neural network models. In order to solve the problem of low accuracy of object detection caused by long-tail data of wild animals， a method based on two-stage learning and re-weighting to solve long-tail data was proposed， and the method was applied to wildlife object detection based on YOLOv4-Tiny. Firstly， a new wildlife dataset with obvious long-tail data characteristics was collected， labelled and constructed. Secondly， a two-stage method based on transfer learning was used to train the neural network. In the first stage， the classification loss function was trained without weighting. In the second stage， two improved re-weighting methods were proposed， and the weights obtained in the first stage were used as the pre-training weights for re-weighting training. Finally， the wildlife test set was used to tested. Experimental results showed that the proposed long-tail data solving method achieved 60.47% and 61.18% mAP （mean Average Precision） with cross-entropy loss function and focal loss function as classification loss respectively， which was 3.30 percentage points and 5.16 percentage points higher than that the no-weighting method under the two loss functions， and 2.14 percentage points higher than that of the proposed improved effective sample weighting method under focus loss function. It shows that the proposed method can improve the object detection performance of YOLOv4-Tiny network for wildlife datasets with long-tail data characteristics.