An identification method for Eolagurus luteus burrows in large-scale UAV images

doi:10.13474/j.cnki.11-2246.2026.0916

Abstract

Abstract: [Purposes]To address the inefficiency of manual detection caused by the small size and wide distribution of Eolagurus luteus burrows,this study aims to develop an automated identification method based on large-scale UAV imagery.[Methods]An end-to-end automated detection framework was proposed,with its core being an improved YOLO-Burrow model.Based on YOLO11n，the model is optimized through a lightweight strategy,the introduction of the Monte Carlo attention (MCA)mechanism,and the adoption of the Shape-IoU loss function.[Findings]Experiments on a self-constructed dataset showed that the model achieved an average precision (AP) of 87%,a reorganized accuracy (RA)of 97%,with a compact model size of only 0.89 MB,significantly outperforming the baseline model.[Conclusions]The proposed framework enables accurate,automatic detection and density assessment of Eolagurus luteus burrows in large-scale UAV images,providing reliable technical support for grassland rodent pest monitoring and control．

Key words: UAV, rodent burrow detection, deep learning, YOLO11n

CLC Number:

P237

Huang Mingqin, An Jiale, Zheng Jianghua, Tuerxun Nigela, Liu Li, Wang Nan, Lin Jun, Liu Xuan, Xu Yapeng, Li Xuan. An identification method for Eolagurus luteus burrows in large-scale UAV images[J]. Bulletin of Surveying and Mapping, 2026, 0(9): 108-114.

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URL: http://tb.chinasmp.com/EN/10.13474/j.cnki.11-2246.2026.0916

http://tb.chinasmp.com/EN/Y2026/V0/I9/108

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