Prairie mouse hole target detection technology based on deep learning

Abstract

Rat hole detection is a key work in the prevention of rat damage. The digital rat hole detection method of UAV combined with target detection is studied to replace the manual carpet rat hole detection, so as to improve the detection efficiency. In this paper, low-altitude remote sensing of unmanned aerial vehicle was used to collect rat hole images on the Edolechuan grassland in Hohhot, Inner Mongolia. Combined with deep learning models: Faster-Rcnn, Yolov3 and SSD, rat hole detection was compared and analyzed. Data was cut and labeled through image preprocessing method. Then, by comparing the three groups of target detection models, the results show that the SSD model has the best effect on rat hole detection, the accuracy rate can reach 91.8%, and the reasoning speed can reach 7.9ms.