地表未爆子弹药快速检测系统设计与实现

doi:10.12305/j.issn.1001-506X.2025.08.21

摘要/Abstract

摘要：

随着无人智能与深度学习技术的快速发展，传统人工检测方式在地表未爆子弹药检测中显得日益低效和受限。针对地面检测速度慢、空基检测误差大等问题，提出一种基于无人机载平台的快速检测系统。系统采用多模成像探测、人工智能目标检测、无人负载巡检的方式，通过无人机载成像探测平台、二维地图快速检测和地面站三个子系统协同工作，实现对地表未爆子弹药的全天时高效检测。实验结果表明，系统在检测速度、精度及环境适应性方面较其他传统检测方法提升效果显著，且无需人工近距离接触，能够有效降低安全风险，为地表未爆子弹药检测提供了一种新的解决方案。

关键词: 地表未爆子弹药, 无人机, 深度学习, 快速检测, 二维地图重建

Abstract:

With the rapid development of unmanned intelligence and deep learning technology, the traditional manual detection method has become increasingly inefficient and limited in the detection of surface unexploded substitutions. In response to the problems of slow ground detection speed and large errors in aerial detection, this paper proposes a rapid detection system based on an unmanned aerial vehicle (UAV) platform. The system adopts a multi-mode imaging detection, artificial intelligence target detection, and unmanned load inspection approach. Through the collaborative work of three subsystems: the UAV-mounted imaging detection platform, the two-dimensional map rapid detection system, and the ground station, it achieves all-day efficient detection of surface unexploded submunitions. Experimental results show that the system significantly improves detection speed, accuracy, and environmental adaptability compared to other traditional detection methods. Moreover, it does not require manual close contact, effectively reducing safety risks, and provides a new solution for the detection of surface unexploded submunitions.

Key words: surface unexploded submunition, unmanned aerial vehicle (UAV), deep learning, rapid detection, two-dimensional map reconstruction

中图分类号:

TP 294

闫小伟, 凌冲, 石胜斌. 地表未爆子弹药快速检测系统设计与实现[J]. 系统工程与电子技术, 2025, 47(8): 2639-2645.

Xiaowei YAN, Chong LING, Shengbin SHI. Design and implementation of a rapid detection system for surface unexploded submunitions[J]. Systems Engineering and Electronics, 2025, 47(8): 2639-2645.

图/表 18

图1

图2

图3

图4

图5

图6

图7

图8

图9

图10

图11

图12

表1

图13

图14

图15

图16

图17

参考文献 29

1	沈莹, 杨晓宝, 王嘉增, 等. 浅海未爆弹磁异常探测和定位技术[J]. 数字海洋与水下攻防, 2023, 6 (5): 534- 541.
	SHEN Y, YANG X B, WANG J Z, et al. Magnetic anomaly detection and localization technology of unexploded ordnance in shallow water[J]. Digital Ocean and Underwater Attack and Defense, 2023, 6 (5): 534- 541.
2	王利杰. 瞬变电磁未爆弹快速探测关键问题研究[D]. 长春: 吉林大学, 2023.
	WANG L J. Research on key issues of fast detection of unexploded ordnance with transient electromagnetic[D]. Changchun: Jilin University, 2023.
3	MILAN B, BOZIDAR P. UAV thermal imaging for unexploded ordnance detection by using deep learning[J]. Remote Sensing, 2023, 15 (4): 967.
4	MEDDA A, FUNK R, AHUJA K, et al. Measurements of infrasound signatures from grenade blast during training[J]. Military Medicine, 2021, 186 (S1): 523- 528.
5	DENG S B, CAI H, LI K, et al. The design and analysis of a light explosive ordnance disposal manipulator[C]//Proc.of the 2nd International Conference on Robotics and Automation Sciences, 2018: 129−133.
6	胡聪, 何晓晖, 邵发明, 等. 基于Faster R-CNN的未爆弹检测[J]. 机电产品开发与创新, 2021, 34 (5): 105- 107. doi: 10.3969/j.issn.1002-6673.2021.05.033
	HU C, HE X H, SHAO F M, et al. Unexploded ordnance detection based on faster R-CNN[J]. Development and Innovation of Electromechanical Products, 2021, 34 (5): 105- 107. doi: 10.3969/j.issn.1002-6673.2021.05.033
7	单成之, 张健. 基于关键点的未爆弹图像目标检测算法[J]. 现代计算机, 2023, 29 (1): 39- 44.
	SHAN C Z, ZHANG J. An algorithm for object detection in unexploded bombs images based on key points[J]. Modern Computers, 2023, 29 (1): 39- 44.
8	曾俊, 卢瑞涛, 杨小冈, 等. 六旋翼无人机空基智能排爆系统设计与实现[J]. 电光与控制, 2023, 30 (5): 61- 65. doi: 10.3969/j.issn.1671-637X.2023.05.012
	ZENG J, LU R T, YANG X G, et al. Design and implementation of air-based intelligent EOD system based on six-rotor UAV[J]. Optoelectronics and Control, 2023, 30 (5): 61- 65. doi: 10.3969/j.issn.1671-637X.2023.05.012
9	EMIL M K, DAVID M W, EDUARDO S D S L, et al. High-speed magnetic surveying for unexploded ordnance using UAV systems[J]. Remote Sensing, 2022, 14 (5): 1134. doi: 10.3390/rs14051134
10	DEVIPRASAD P V, GHADI H, DAS D, et al. High performance short wave infrared photodetector using p-i-p quantum dots （InAs/GaAs） validated with theoretically simulated model[J]. Journal of Alloys and Compounds, 2019, 80 (4): 18- 26.
11	YAN Y P, WANG G J, LI S Y, et al. Delensing of cosmic microwave background polarization with machine learning[J]. The Astrophysical Journal Supplement Series, 2023, 267, 1. doi: 10.3847/1538-4365/acd051
12	邓锴. 基于GIS平台二维地图重建技术研究与开发[D]. 北京: 北京工业大学, 2018.
	DENG K. Research and development of 2D map reconstruction technology based on GIS platform[D]. Beijing: Beijing University of Technology, 2018.
13	王凌云, 尹海波, 王琪. SURF和RANSAC在图像拼接中的应用[J]. 电子测量技术, 2016, 39 (4): 71- 75. doi: 10.3969/j.issn.1002-7300.2016.04.017
	WANG L Y, YIN H B, WANG Q. Application of SURF and RANSAC algorithm on image stitching[J]. Electronic Measurement Techniques, 2016, 39 (4): 71- 75. doi: 10.3969/j.issn.1002-7300.2016.04.017
14	王洪斌, 于菲, 李一骏, 等. 分块特征匹配与局部差分结合的运动目标检测[J]. 计量学报, 2015, 36 (4): 352- 355. doi: 10.3969/j.issn.1000-1158.2015.04.04
	WANG H B, YU F, LI Y J, et al. Moving target detection based on block feature matching and local difference[J]. Acta Metrologica Sinica, 2015, 36 (4): 352- 355. doi: 10.3969/j.issn.1000-1158.2015.04.04
15	WANG Z B, YU A X, DONG Z, et al. Performance evaluation of interest point detectors for heterologous image matching[J]. Remote Sensing, 2022, 14 (15): 3724.
16	LOWE G D. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60 (2): 91- 110. doi: 10.1023/B:VISI.0000029664.99615.94
17	赵春江. 图像局部特征检测和描述——基于OpenCV源码分析的算法与实现[M]. 北京: 人民邮电出版社, 2018.
	ZHAO C J. Local image feature detection and description algorithms and implementation based on OpenCV source code analysis[M]. Beijing: Post and Telecommunications Publishing House, 2018.
18	孙希延, 刘博, 纪元法, 等. 基于SIFT改进的无人机图像匹配算法[J]. 电光与控制, 2023, 30 (5): 34- 38. doi: 10.3969/j.issn.1671-637X.2023.05.007
	SUN X Y, LIU B, JI Y F, et al. Improved UAV image matching algorithm based on SIFT[J]. Optoelectronics and Control, 2023, 30 (5): 34- 38. doi: 10.3969/j.issn.1671-637X.2023.05.007
19	BAY H, ESS A, TUYTELAARS T, et al. SURF: speeded up robust features[J]. Computer Vision and Image Understanding, 2008, 110 (3): 346- 359. doi: 10.1016/j.cviu.2007.09.014
20	WANG R, BAO L L, CAI Y X. Image feature extraction and matching of augmented solar images in space weather[J]. Journal of Space Science, 2023, 43 (5): 840- 852.
21	HUANG Q X, XIANG T, ZHAO Z H, et al. Directional region-based feature point matching algorithm based on SURF[J]. Journal of the Optical Society of America, 2024, 41 (2): 157- 164.
22	YOU Y, GUAN H L, DUAN F Z, et al. A light field full-focus image feature point matching method with an improved ORB algorithm[J]. Sensors, 2022, 23 (1): 123- 123. doi: 10.3390/s23010123
23	苑朝, 黄诺飞, 蒋阳, 等. 基于改进旋转不变性二进制描述算法的电力场景图像拼接[J]. 电力科学与工程, 2024, 40 (1): 31- 38. doi: 10.3969/j.ISSN.1672-0792.2024.01.004
	YUAN C, HUANG N F, JIANG Y, et al. Power scene images mosaic based on improved oriented fast and rotated brief algorithm[J]. Electric Power Science and Engineering, 2024, 40 (1): 31- 38. doi: 10.3969/j.ISSN.1672-0792.2024.01.004
24	YAN H A, WANG J, ZHANG P. Application of optimized ORB algorithm in design AR augmented reality technology based on visualization[J]. Mathematics, 2023, 11 (6): 1278. doi: 10.3390/math11061278
25	QIAN Y L, SHI H B, LIU G C. A method of UAV visible light remote sensing image registration based on eigenvector technique[J]. Results in Engineering, 2023, 20, 101601.
26	JIN B, HU W L, WANG H Q. Human interaction recognition based on transformation of spatial semantics[J]. IEEE Signal Processing Letters, 2012, 19 (3): 139- 142. doi: 10.1109/LSP.2012.2183364
27	TLEBALDINOVA A, DENISSOVA N, BAKLANOVA O, et al. Normalization of vehicle license plate images based on analyzing of its specific features for improving the quality recognition[J]. Acta Polytechnica Hungarica, 2020, 17 (6): 193- 206. doi: 10.12700/APH.17.6.2020.6.11
28	ZHAO L K, ZHENG S Y, YANG W J, et al. An image thresholding approach based on Gaussian mixture model[J]. Pattern Analysis and Applications, 2019, 22 (1): 75- 88.
29	王志锐. 基于深度神经网络的异源遥感图像变化检测[D]. 西安: 西安电子科技大学, 2018.
	WANG Z R. Change detection for hetergeneous remote sensing image based on deep neural network[D]. Xi’an: Xidian University, 2018.

算法	处理时间/s	特征点数量/个	互信息/%	均方根误差/像素	鲁棒性
SIFT	0.154 0	355	85.41	0.94	中
ORB	0.094 6	214	69.18	1.14	良
SURF	0.120 6	297	81.34	0.87	优

[1]	张欣悦, 吴晓莉, 王名珺, 晏彪, 武愈涵. 有/无人机协同操作界面的最佳交互方式评估[J]. 系统工程与电子技术, 2025, 47(8): 2600-2611.
[2]	李延通, 李子璠, 周姗姗, 张闯. 无人机光伏电站巡检双目标选址-路径问题研究[J]. 系统工程与电子技术, 2025, 47(8): 2612-2621.
[3]	羊钊, 胡锦标, 王艳, 齐洪彪. 考虑异巢起降的无人机山地巡检覆盖路径规划[J]. 系统工程与电子技术, 2025, 47(8): 2622-2631.
[4]	符小卫, 王辛夷, 乔哲. 基于APIQ算法的多无人机攻防对抗策略[J]. 系统工程与电子技术, 2025, 47(7): 2205-2215.
[5]	郑凯文, 杜承泽, 赵兴芳, 逄晓凡. 融合时空散列的三维RRT^*多编队航路规划[J]. 系统工程与电子技术, 2025, 47(7): 2256-2266.
[6]	吴北苹, 何晶, 党慧莹, 岳地久. 基于作战环的反无人机作战体系贡献率评估[J]. 系统工程与电子技术, 2025, 47(7): 2267-2274.
[7]	林思颖, 郁丰, 熊智, 吴方, 周紫君. 基于AHRS的GNSS间断拒止下低成本无人机导航方法[J]. 系统工程与电子技术, 2025, 47(7): 2329-2338.
[8]	唐俊超, 胡春鹤. 三维地形风场环境下无人机全覆盖路径规划[J]. 系统工程与电子技术, 2025, 47(7): 2349-2356.
[9]	何云风, 史贤俊, 卢建华, 赵超轮, 赵国荣. 故障条件下基于同步DMPC的多无人机分组编队控制[J]. 系统工程与电子技术, 2025, 47(7): 2357-2370.
[10]	张琬滢, 高由兵, 李泽一, 李鹏飞, 张伟. 基于双路径特征融合网络的背景信号抑制算法[J]. 系统工程与电子技术, 2025, 47(7): 2406-2413.
[11]	张新征, 闫梦可, 朱晓林. 噪声伪标签容忍的半监督SAR目标识别[J]. 系统工程与电子技术, 2025, 47(6): 1796-1805.
[12]	符小卫, 王辛夷, 乔哲. 基于ASDDPG算法的多无人机对抗策略[J]. 系统工程与电子技术, 2025, 47(6): 1867-1879.
[13]	姜智杰, 宋恒, 胡楠, 段兰茜, 曹平. 隧道环境毫米波雷达目标识别与分类算法[J]. 系统工程与电子技术, 2025, 47(5): 1453-1460.
[14]	崔瑞靖, 孙建彬, 杨克巍, 李明浩. 基于UAF的装备作战试验指标体系构建方法[J]. 系统工程与电子技术, 2025, 47(5): 1536-1550.
[15]	宋怡成, 齐瑞云. 通信故障下基于一跳无人机编队拓扑重构[J]. 系统工程与电子技术, 2025, 47(5): 1706-1717.