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

doi:10.6046/zrzyyg.2022018

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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.

Keywords dead tree YOLOv4-tiny attention mechanism SPP ELU activation function

ZTFLH:

TP751

Issue Date: 20 March 2023

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	Yuanhang JIN
	Maolin XU
	Jiayuan ZHENG

Cite this article:

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

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022018 OR https://www.gtzyyg.com/EN/Y2023/V35/I1/90

Fig.1 Flow chart of dead tree detection technology

Fig.2 SPP structure with fixed size output

Fig.3 Comparison between activation function LeakyReLU and ELU

Fig.4 ECA attention module structure

Fig.5 Improved YOLOv4 tiny structure

Fig.6 Partial dataset image

Fig.7 Data enhancement

Tab.1 Results of different attention mechanisms

Fig.8 Comparison of partial detection results

Tab.2 Comparison of different activation functions

Tab.3 Comparison of test results of 2 models

Fig.9 Different state partial images

Tab.4 Comparison of four models for different image detection results(%)

Fig.10 The change trend of Loss in model training

Tab.5 The results of the 4 models were compared

Tab.6 Comparison of different Batch Size

Tab.7 Comparison of different data sets

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