A dead tree detection algorithm based on improved YOLOv4-tiny for UAV images

JIN Yuanhang; XU Maolin; ZHENG Jiayuan

doi:10.6046/zrzyyg.2022018

2023 Vol. 35, No. 1

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JIN Yuanhang, XU Maolin, ZHENG Jiayuan. 2023. A dead tree detection algorithm based on improved YOLOv4-tiny for UAV images. Remote Sensing for Natural Resources, 35(1): 90-98. doi: 10.6046/zrzyyg.2022018

Citation:

JIN Yuanhang, XU Maolin, ZHENG Jiayuan. 2023. A dead tree detection algorithm based on improved YOLOv4-tiny for UAV images. Remote Sensing for Natural Resources, 35(1): 90-98. doi: 10.6046/zrzyyg.2022018

A dead tree detection algorithm based on improved YOLOv4-tiny for UAV images

School of Civil Engineering, Liaoning University of Science and Technology, Anshan 114051, China

Received Date: 12 January 2022

Revised Date: 15 March 2023

Publish Date: 20 March 2023

Abstract

Abstract

The current dead tree detection primarily relies on manual field surveys and, thus, is limited by forest topography, suffers a low detection efficiency, and is dangerous. Given these problems, this study proposed a YOLOv4-tiny dead tree detection algorithm based on the attention mechanism and spatial pyramid pooling (SPP) and improved the original detection model. First, the SPP structure was introduced after the Backbone part of the model to combine local and global features and enrich the feature representation capability of the model. Then, the original activation function LeakyReLU in the model was replaced with ELU, which made the activation function saturate unilaterally, thus improving the convergence and robustness of the model. Finally, the attention mechanism ECANet was introduced into the model to enhance the capacity of the network to learn important information in images, thus improving the performance of the network. The images of trees in a mountain forest of a scenic area in southern Liaoning were collected using an unmanned aerial vehicle (UAV). Then, dead trees in these images were detected using different models. The detection results show that the improved algorithm had a detection accuracy of 93.25%, which was improved by 9.58%, 12.57%, 10.54%, and 4.87% than that of the YOLOv4-tiny, YOLOv4, and SSD algorithms and an algorithm stated in literature [8], respectively, and achieved the effective detection of dead trees.
- dead tree /
- YOLOv4-tiny /
- attention mechanism /
- SPP /
- ELU activation function

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References

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