In this paper, we propose the construction of an extended YOLOv5 model using the infrared camera image datasets of five species to achieve the automatic recognition of massive wild animal images in real-time, accuracy on resource-limited platforms such as infrared cameras. Furthermore, we improve the negative load and low timeliness of data transmission in wildlife monitoring. Here, the dataset constructed is used to train four network structures, namely, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x. By comparing the accuracy, detection speed, and volume of different network structures, the optimal network structure was determined. Simultaneously, we analyzed the recognition effect of the model under the interference of complex background information to evaluate the applicability of YOLOv5 in real-field scenes. Compared with similar algorithms, the advantages of YOLOv5 for wildlife recognition outweighed others. The experimental results show that the recognition accuracy of the four network structures was high. Moreover, F1-score and average accuracy (mAP) were more than 90%, and the comprehensive performance of YOLOv5m was the best. However, YOLOv5 still has a good recognition effect under the interference of several complex background information and can adapt to real-field scenes. Compared with other algorithms, YOLOv5 has the advantages of high precision, strong robustness, and low resource occupation. It is a lightweight model with superior performance, which provides a new opportunity for real-time wildlife identification on resource-constrained platforms.