Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Automatic Detection of Dead Trees Based on Lightweight YOLOv4 and UAV Imagery

  • Yuanhang Jin (State Key Laboratory of Geo-Information Engineering and School of Civil Engineering, University of Science and Technology Liaoning) ;
  • Maolin Xu (State Key Laboratory of Geo-Information Engineering and School of Civil Engineering, University of Science and Technology Liaoning) ;
  • Jiayuan Zheng (State Key Laboratory of Geo-Information Engineering and School of Civil Engineering, University of Science and Technology Liaoning)
  • Received : 2022.06.21
  • Accepted : 2022.11.28
  • Published : 2023.10.31
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Abstract

Dead trees significantly impact forest production and the ecological environment and pose constraints to the sustainable development of forests. A lightweight YOLOv4 dead tree detection algorithm based on unmanned aerial vehicle images is proposed to address current limitations in dead tree detection that rely mainly on inefficient, unsafe and easy-to-miss manual inspections. An improved logarithmic transformation method was developed in data pre-processing to display tree features in the shadows. For the model structure, the original CSPDarkNet-53 backbone feature extraction network was replaced by MobileNetV3. Some of the standard convolutional blocks in the original extraction network were replaced by depthwise separable convolution blocks. The new ReLU6 activation function replaced the original LeakyReLU activation function to make the network more robust for low-precision computations. The K-means++ clustering method was also integrated to generate anchor boxes that are more suitable for the dataset. The experimental results show that the improved algorithm achieved an accuracy of 97.33%, higher than other methods. The detection speed of the proposed approach is higher than that of YOLOv4, improving the efficiency and accuracy of the detection process.

Keywords

Acknowledgement

This research was supported by the Fund project of the Provincial Education Department (No. LJKMZ20220638) and the Open Fund Project of the Marine Information Technology Innovation Center of the Ministry of Natural Resources.

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