空地协同场景下特殊障碍物数据集与检测算法评估系统构建

冷呈宇; 赵津; 刘畅; 杨世凤

doi:10.12382/bgxb.2024.0583

您当前的位置：

首页 >

文章列表页 >

空地协同场景下特殊障碍物数据集与检测算法评估系统构建

更新时间：2025-09-05

- 空地协同场景下特殊障碍物数据集与检测算法评估系统构建
- Construction of Special Obstacle Dataset and Detection Algorithm Evaluation System in Air-ground Collaborative Scenarios
- 兵工学报 2025年46卷第6期页码：240583
- 作者机构：
  
  贵州大学机械工程学院, 贵州贵阳 550025
- 作者简介：
  
  *邮箱:zhaoj@gzu.edu.cn
- 基金信息：
  
  国家自然科学基金项目(51965008);贵州省高层次创新型人才(百层次)项目(GCC[2023]016)
- DOI：10.12382/bgxb.2024.0583
  中图分类号：
- 收稿：2024-07-15，
  
  网络出版：2025-06-28，
  
  纸质出版：2025-06-10
- 稿件说明：
移动端阅览
冷呈宇, 赵津, 刘畅, 等. 空地协同场景下特殊障碍物数据集与检测算法评估系统构建[J]. 兵工学报, 2025,46(6):240583.

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.
冷呈宇, 赵津, 刘畅, 等. 空地协同场景下特殊障碍物数据集与检测算法评估系统构建[J]. 兵工学报, 2025,46(6):240583. DOI： 10.12382/bgxb.2024.0583.

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.

关键词

Keywords

references

MASIHULLAH S , GARG R , MUKHERJEE P , et al . Attention based coupled framework for road and pothole segmentation [C ] // Proceedings of the 2020 25th International Conference on Pattern Recognition . Milan,Italy : IEEE , 2021 : 5812 - 5819 .

SUN X Z , XIE Y C , JIANG L M , et al . DMA-Net:deeplab with multi-scale attention for pavement crack segmentation [J ] . IEEE Transactions on Intelligent Transportation Systems , 2022 , 23 ( 10 ): 18392 - 18403 .

LIU Z J , FAN G Y , RAO L , et al . Positive and negative obstacles detection based on dual-LiDAR in field environments [J ] . IEEE Robotics and Automation Letters , 2024 , 9 ( 8 ): 6768 - 6775 .

RAN H H , SUN L , CHENG S , et al . A novel cooperative searching architecture for multi-unmanned aerial vehicles under restricted communication [J ] . Asian Journal of Control . 2022 , 24 ( 2 ): 510 - 516 .

LIU C , ZHAO J , SUN N Y . A review of collaborative air-ground robots research [J ] . Journal of Intelligent & Robotic Systems , 2022 , 106 ( 3 ): 1 - 23 .

ZHU Y L , KONG Y , JIE Y G , et al . GRACO:a multimodal dataset for ground and aerial cooperative localization and mapping [J ] . IEEE Robotics and Automation Letters , 2023 , 8 ( 2 ): 966 - 973 .

GUO S C , LI J H , FENG Y , et al . UDTIRI:an online open-source intelligent road inspection benchmark suite:arXiv:2304.08842 [R/OL ] . Ithaca,NY,US:Cornell Universit , 2023 :2304.08842. https://arxiv.org/abs/2304.08842 https://arxiv.org/abs/2304.08842 https://arxiv.org/abs/2304.08842.

ARYA D , MAEDA H , GHOSH S K , et al . Global road damage detection:state-of-the-art solutions [C ] // Proceedings of the 2020 IEEE International Conference on Big Data.Atlanta,GA,US:IEEE , 2020 : 5533 - 5539 .

KULAMBAYEV B , NURLYBEK M , ASTAUBAYEVA G , et al . Real-time road surface damage detection framework based on mask R-CNN model [J ] . International Journal of Advanced Computer Science and Applications , 2023 , 14 ( 9 ): 757 - 765 .

HERNANDA Z S , MAHMUDAH H , SUDIBYO R W . CNN-based hyperparameter optimization approach for road pothole and crack detection systems [C ] // Proceedings of the 2022 IEEE World AI IoT Congress.Seattle,WA,US:IEEE , 2022 : 538 - 543 .

FENG Z , GUO Y N , SUN Y X . Segmentation of road negative obstacles based on dual semantic-feature complementary fusion for autonomous driving [J ] . IEEE Transactions on Intelligent Vehicles , 2024 , 9 ( 4 ): 4687 - 4697 .

FENG Z , GUO Y N , NAVARRO-ALARCON D , et al . InconSeg:residual-guided fusion with inconsistent multi-modal data for negative and positive road obstacles segmentation [J ] . IEEE Robotics and Automation Letters , 2023 , 8 ( 8 ): 4871 - 4878 .

赵一兵 , 郭烈 , 李明丽 , 等 . 无人地面车辆野外水体障碍物识别 [J ] . 兵工学报 , 2014 , 35 ( 增刊1 ): 62 - 67 .

ZHAO Y B , GUO L , LI M L , et al . Recognition of water obstacles for unmanned ground vehicles in the field [J ] . Acta Armamentarii , 2014 , 35 ( S1 ): 62 - 67 . (in Chinese)

刘家银 , 唐振民 , 王安东 , 等 . 基于多激光雷达与组合特征的非结构化环境负障碍物检测 [J ] . 机器人 , 2017 , 39 ( 05 ): 638 - 651 .

LIU J Y , TANG Z M , WANG A D , et al . Detection of negative obstacles in unstructured environments based on multi-lidar and combined features [J ] . Robot , 2017 , 39 ( 5 ): 638 - 651 . (in Chinese)

WANG H , ZHANG Q , FAN Z Q , et al . Towards an obstacle detection system for robot obstacle negotiation [J ] . Industrial Robot-the International Journal of Robotics Research and Application , 2024 , 51 ( 2 ): 236 - 245 .

RUAN S L , LI S B , LU C W , et al . A real-time negative obstacle detection method for autonomous trucks in open-pit mines [J ] . Sustainability , 2022 , 15 ( 1 ): 120 .

阮顺领 , 鄢盛钰 , 顾清华 , 等 . 基于多特征融合的露天矿区道路负障碍检测 [J ] . 煤炭学报 , 2024 , 49 ( 5 ): 2561 - 2572 .

RUAN S L , YAN S Y , GU Q H , et al . Obstacle detection on open-pit mining roads based on multi-feature fusion [J ] . Journal of China Coal Society , 2024 , 49 ( 5 ): 2561 - 2572 . (in Chinese)

DODGE D , YILMAZ M . Convex vision-based negative obstacle detection framework for autonomous vehicles [J ] . IEEE Transactions on Intelligent Vehicles , 2022 , 8 ( 1 ): 778 - 789 .

付浩龙 , 赵津 , 席阿行 , 等 . 无人机视觉下基于改进OTSU的负障碍物检测 [J ] . 计算机应用与软件 , 2020 , 37 ( 9 ): 105 - 110 .

FU H L , ZHAO J , XI A X , et al . Obstacle detection based on improved OTSU in drone vision [J ] . Journal of Computer Applications and Software , 2020 , 37 ( 9 ): 105 - 110 . (in Chinese)

GOODIN C , CARRILLO J , MONROE J G , et al . An analytic model for negative obstacle detection with lidar and numerical validation using physics-based simulation [J ] . Sensors , 2021 , 21 ( 9 ): 3211 .

金政北 , 金贝贝 , 宋晓辉 , 等 . 改进YOLOv10算法及其在路面坑洼检测中的应用 [J/OL ] . 计算机应用与软件 , 2025 ( 2025-03-11 ). http://kns.cnki.net/kcms/detail/31.1260.tp.20250307.0824.002.htm http://kns.cnki.net/kcms/detail/31.1260.tp.20250307.0824.002.htm http://kns.cnki.net/kcms/detail/31.1260.tp.20250307.0824.002.htm.

JIN Z B , JIN B B , SONG X H , et al . Improvement of YOLOv10 algorithm and its application in pavement pothole detection [J/OL ] . Computer Applications and Software , 2025 ( 2025-03-11 ). http://kns.cnki.net/kcms/detail/31.1260.tp.20250307.0824.002.htm http://kns.cnki.net/kcms/detail/31.1260.tp.20250307.0824.002.htm http://kns.cnki.net/kcms/detail/31.1260.tp.20250307.0824.002.htm. (in Chinese)

LIN T Y , MAIRE M , BELONGIE S , et al . Microsoft coco:common objects in context [C ] // Proceedings of the 13th European Conference on Computer Vision.Zurich,Switzerland:Springer , 2014 : 740 - 755 .

浏览量

190

下载量

CNKI被引量

文章被引用时，请邮件提醒。

提交

工具集

关联资源

暂无数据