Grassland plant identification based on unmanned aerial vehicle imagery and improved YOLOv8 network

Zhicheng Chen; Lin Sun; Xiaofang Wang; Huawei Wan; Fengming Wan

doi:10.1117/12.3032802

5 July 2024 Grassland plant identification based on unmanned aerial vehicle imagery and improved YOLOv8 network

Zhicheng Chen, Lin Sun, Xiaofang Wang, Huawei Wan, Fengming Wan

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 131842I (2024) https://doi.org/10.1117/12.3032802
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

The automatic identification of grassland plants based on UAV remote sensing imagery is crucial for the management and conservation of grassland ecosystems. Grassland plants, characterized by small individual sizes, complex backgrounds, and dense distribution, pose a challenge for UAV image identification. An improved YOLOv8 algorithm is proposed in this study to enhance the detection effect of grassland plants from different angles and optimize the detection of small targets in complex backgrounds. Firstly, a rotation-invariant mechanism is introduced into the backbone to improve the model’s adaptation to grassland plant targets from various angles, thereby improving detection accuracy and robustness. Secondly, a self-attention mechanism is applied in the backbone to enhance the model’s understanding capability of correlations between different plants. Additionally, a multi-level feature extraction structure based on dilated convolution is employed in the neck to further enhance the model’s feature representation capability. Finally, the SlideLoss loss function is introduced to solve the problems of insufficient sample size and foreground-background detection impact. Experimental results demonstrate that the improved algorithm achieves remarkable results in the automatic identification of grassland plants in Inner Mongolia, validating the application prospects of UAV remote sensing technology in grassland plant species monitoring.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhicheng Chen, Lin Sun, Xiaofang Wang, Huawei Wan, and Fengming Wan "Grassland plant identification based on unmanned aerial vehicle imagery and improved YOLOv8 network", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 131842I (5 July 2024); https://doi.org/10.1117/12.3032802

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KEYWORDS

Unmanned aerial vehicles

Feature extraction

Target detection

Convolution

Remote sensing

Vegetation

Ecosystems

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