UAV Intelligent Forest Inspection System Based on Computer Vision

Abstract

The forestry inspection adopted in China at this stage is still mainly based on manual inspection, which is not only inefficient, but also labor-intensive and has high safety risks for the inspectors. Therefore, this paper proposes a UAV intelligent forest inspection system based on computer vision. However, the application of this UAV intelligent forestry inspection system in complex scenarios has the problems of low model detection accuracy and slow detection speed. To address the problem of low model detection accuracy due to unbalanced data categories and inaccurate labeling, this paper mainly proposes a data cleaning algorithm based on image operation to improve the quality of data labeling, thus improving the object detection accuracy. To address the problem of slow detection speed, this paper proposes a light-weight structure improvement algorithm based on SOLOv2, which uses grouped convolution to improve the detection speed on the basis of ensuring the object detection accuracy, and uses TensorRT for hardware acceleration. Experiments show that the detection accuracy of the improved model is improved by 1.7%, and the reasoning speed is improved by 14.2 frames.