Lightweight detection model for typical environmental terrain target in Tibetan Plateau

doi:10.3785/j.issn.1008-973X.2026.03.015

Journal of ZheJiang University (Engineering Science)

2026, Vol. 60

Issue (3): 594-603 DOI: 10.3785/j.issn.1008-973X.2026.03.015

Lightweight detection model for typical environmental terrain target in Tibetan Plateau

Weiqun LUO1(

),Jingwei LU1,Jiadi WU1,Yuying LIANG1,Chuanpeng SHEN1,Rui ZHU1,2,*(

)

1. College of Information Engineering, Xizang Minzu University, Xianyang 712082, China
2. School of Aerospace Engineering, Xiamen University, Xiamen 361102, China

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Abstract

An obstacle detection model AO-YOLO was proposed in order to solve the problems of low accuracy and excessive calculation of obstacle recognition in traditional methods faced by general aviation aircraft in plateau mountain application scenarios. A multi-scale expanded attention mechanism was introduced by optimizing the existing YOLOv8n model in order to enhance the ability of Neck network to fuse features of different scales. HGBlock structure was used to replace the original Bottleneck module, and lightweight convolution was used to establish the feature hierarchical relationship, so that the network can extract local and global context information at the same time. The memory-efficient attention module and lightweight convolution module were combined to design a new detection head in in order to reduce the number of model parameters and computational cost. The experimental results showed that the mAP@0.5 index of AO-YOLO increased by 2.7% and the mAP@0.5:0.95 index increased by 2.0% on the constructed plateau mountain obstacle dataset (FOD) compared with YOLOv8n, and the overall calculation amount reduced by 24.7%. The proposed model has the characteristics of high accuracy and lightweight in the highland aviation obstacle detection task.

Key words： plateau aviation obstacle detection lightweight YOLOv8n multi-scale dilated attention feature extraction

Received: 30 January 2025 Published: 04 February 2026

CLC:	TP 183
	TP 391

Fund: 西藏自治区重点研发计划资助项目（XZ202401ZY0102,XZ202403ZY0019,XZ202402ZY0017）；厦门市自然科学基金资助项目（3502Z20227179）；教育部人文社会科学规划基金资助项目（23XZJAZH001）；西藏自治区自然科学基金重点资助项目（XZ202401ZR0055）；福建省自然科学基金资助项目（2022J01058）；中国航空科学基金资助项目（2023Z032068001）；水声对抗技术重点实验室基金资助项目（JCKY2024207CH05）；西藏民族大学基金资助项目（Y2024050）.

Corresponding Authors: Rui ZHU E-mail: 1034228464@qq.com;zhurui@xmu.edu.cn

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Cite this article:

Weiqun LUO,Jingwei LU,Jiadi WU,Yuying LIANG,Chuanpeng SHEN,Rui ZHU. Lightweight detection model for typical environmental terrain target in Tibetan Plateau. Journal of ZheJiang University (Engineering Science), 2026, 60(3): 594-603.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2026.03.015 OR https://www.zjujournals.com/eng/Y2026/V60/I3/594

藏区高原典型环境地形目标的轻量化检测模型

为了解决通用航空飞行器在高原山区应用场景中，所面临的传统方法障碍物识别精度低和计算量过大等问题，提出障碍物检测模型AO-YOLO. 通过优化现有的YOLOv8n模型，引入多尺度扩张注意力机制，增强Neck网络对不同尺度特征的融合能力. 采用HGBlock结构替换原有的Bottleneck模块，通过轻量级卷积建立特征层次化关系，使网络能够同时提取局部与全局上下文信息. 结合内存高效的注意力模块与轻量级卷积模块，设计新的检测头，降低模型参数量与计算成本. 实验结果显示，在构建的高原山区障碍物数据集（FOD）上，相较于YOLOv8n，AO-YOLO的mAP@0.5指标提升了2.7%，mAP@0.5:0.95指标提升了2.0%，整体计算量减少了24.7%. 该模型在高原航空障碍物检测任务中具有精度较高和轻量化的特性.

关键词： 高原航空, 障碍物检测, 轻量化YOLOv8n, 多尺度扩张注意力, 特征提取

Fig.1 Structure diagram of AO-YOLO network

Fig.2 Structure of C2f_MS module

Fig.3 Schematic of receptive field controlled by expansion rate

Fig.4 Structure diagram of PP-HGNetV2

Fig.5 Structure diagram of HGBlock module

Fig.6 Refactored backbone network structure

Fig.7 Structure of SADetect

Tab.1 Setting of training parameter

Tab.2 Model comparison experiment conducted on FOD dataset

Fig.8 Precision-recall curve of YOLOv8n

Fig.9 Precision-recall curve of AO-YOLO

Tab.3 Ablation experiment of AO-YOLO model

Fig.10 Comparison of different attention mechanism

Tab.4 Experimental result on VisDrone2019

Tab.5 Experimental result on NWPU VHR-10

Fig.11 Visual comparison of detection result


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