Target detection of Ochotona curzoniae is the basis for its population statistics and population dynamic changes research， but the traditional intelligent monitoring systems of Ochotona curzoniae has a large target detection hardware equipment and weak mobility in sampling and collecting data. To solve the above problems， an improved YOLOv3? based target detection method that can be deployed to the portable device Jetson TX2 was proposed. The lightweight Ochotona curzoniae target detection model was constructed by replacing Darknet53， backbone network of YOLOv3， with MobileNet and using pruning and fine?tuning methods to further reduce the model size. Next， the model was deployed on the portable target detection device Jetson TX2. Experimental results of Ochotona curzoniae target detection in natural scenes show that the proposed method has the detection Average Precision （AP）， detection Frames per Second （FPS） and model size of 97.36%， 36 and 14.88 MB， respectively， which are better than those of the improved YOLOv3 model without pruning and the original YOLOv3 model； with the AP only reduced by 1.05 percentage points， the proposed method has the detection speed improved by 620% and the model size compressed by 93.67%， which can be deployed to portable devices for real-time and accurate detection of Ochotona curzoniae.