Construction of Special Obstacle Dataset and Detection Algorithm Evaluation System in Air-ground Collaborative Scenarios

Chengyu LENG; Jin ZHAO; Chang LIU; Shifeng YANG

doi:10.12382/bgxb.2024.0583

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Construction of Special Obstacle Dataset and Detection Algorithm Evaluation System in Air-ground Collaborative Scenarios

更新时间：2025-09-05

- Construction of Special Obstacle Dataset and Detection Algorithm Evaluation System in Air-ground Collaborative Scenarios
- Acta Armamentarii Vol. 46, Issue 6, Pages: 240583(2025)
- 作者机构：
  
  贵州大学机械工程学院, 贵州贵阳 550025
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2024.0583
  CLC：
- Received：15 July 2024，
  
  Published Online：28 June 2025，
  
  Published：10 June 2025
- 稿件说明：
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Chengyu LENG, Jin ZHAO, Chang LIU, et al. Construction of Special Obstacle Dataset and Detection Algorithm Evaluation System in Air-ground Collaborative Scenarios[J]. Acta Armamentarii, 2025, 46(6): 240583.
DOI：

Chengyu LENG, Jin ZHAO, Chang LIU, et al. Construction of Special Obstacle Dataset and Detection Algorithm Evaluation System in Air-ground Collaborative Scenarios[J]. Acta Armamentarii, 2025, 46(6): 240583. DOI： 10.12382/bgxb.2024.0583.

摘要

在空地协同场景下

特殊障碍物的识别与处理对地面装备安全运行至关重要。针对非结构化环境中样本稀缺的问题

构建了包含33 124张图像的检测数据集

覆盖多类典型特殊障碍物

支持复杂场景下的识别任务。为准确评估检测算法性能

设计融合类别信息与定位精度的综合评价指标

增强模型对比的科学性。提出结合物理属性与环境语义的可通行性分析方法

为地面无人系统路径规划提供依据。实验结果表明

该数据集与评估体系显著提升检测精度

所提方法能有效识别坑洞、水面等典型特殊障碍物。

Abstract

The recognition and handling of special obstacles are critical to ensuring the safe operation of ground equipment in air-ground collaborative scenarios.To address the lack of data samples in unstructured environments

a detection dataset comprising 33124 images is developed

featuring a wide range of typical special obstacles to support the recognition tasks in complex scenes.A comprehensive evaluation index integrating the category information and localization accuracy is designed to enhance the objectivity and reliability of algorithm performance comparisons.Additionally

a passability analysis method is proposed

which combines the physical properties with the environmental semantics to guide path planning for unmanned ground systems.Experimental results demonstrate that the proposed dataset and evaluation framework significantly improve detection accuracy

and the method effectively identifies typical obstacles such as potholes and water surfaces in unstructured environments.

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references

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