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Dynamic Beehive Detection and Tracking System Based on YOLO V5 and Unmanned Aerial Vehicle
Gao Peng,이강빈,쿠스위디얀토 루카스 위쿠,유승화,Hu Kai,Liang Gaotian,Chen Yufeng,Wang Weixing,Liao Fei,정유석,전문석,최인찬,한웅철 한국농업기계학회 2022 바이오시스템공학 Vol.47 No.4
Purpose With urban development and improvements in human living conditions, wild beehives in densely populated areas present a threat to human safety. The traditional manual method of clear beehives may result in secondary injury to humans. Methods This paper proposes a beehive detection model based on YOLO V5 by introducing the Shufe Block V2 and depthwise separable convolution (DSC) modules to decrease the original model parameters. The model can be deployed on edge computation devices such as Raspberry 4B with good detection accuracy. The PID algorithm and dual servo motors were combined with the object detection model to track the beehive automatically. The results of experiments showed that the inference speed of the improved beehive detection model was 92.5% faster than the original YOLO V5s model, although the detection accuracy and other indicators were not signifcantly diferent. Results The accuracy of the system in this study was as high as 96% in real-time detection, and the maximum recognition distance was 2.5 m. The performance test results of the system deployed on an unmanned aerial vehicle (UAV) showed that 90% of the beehive tracking process could be completed within 2 s, positioning the object in the center of the images collected by the camera. At the same time, when the UAV was moving at random, the detection and tracking system could still follow the beehive quickly and automatically. Conclusion The detection model and tracking system established in this paper provide important support to reduce the secondary damage to the rescue workers that may occur in beehive governance.
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