关键词: 高原鼠兔, Jetson TX2, 轻量化, 目标检测, 模型剪枝

Abstract: Target detection of Ochotona curzoniae is the basis of population statistics and population dynamics. In the traditional intelligent monitoring system of Ochotona curzoniae, the target detection hardware equipment is often large and has weak mobility when sampling and collecting data. To solve above problems, a portable target detection method of Ochotona curzoniae was proposed. Firstly, a lightweight Ochotona curzoniae target detection model was constructed by replacing YOLOv3 backbone network Darknet53 with MobileNet. Secondly, the model was further lightweight by pruning and fine-tuning. Finally, the pruned and fine tuned model was deployed on the portable target detection device Jetson TX2. The experimental results of Ochotona curzoniae target detection in natural scene show that the average detection accuracy, detection speed and model size of the proosed method are 97.36%, 36 frames per second and 14.88M(MByte) respectively, which is better than the uncut model (average detection accuracy: 97.30%, detection speed: 14 frames per second, model size: 92.8M). At the same time, compared with the original (detection accuracy: 98.41%, detection speed: 5 frames per second, model size: 235M), when the average accuracy of the proposed method is reduced by 1.05%, the detection speed is improved by 620% and the model size is compressed by 93.7%. Compared with the existing lightweight models, the average detection accuracy, detection speed and model size are significantly improved. 

Key words: Ochotona curzoniae, Jetson TX2, lightweight, target detection, model pruning