Design and Application of Vehicle Mounted Industrial CT Nondestructive Testing System and Underwater Unmanned Equipment Defect Recognition Software

MA Junli; WANG Shuai; LIU Wenbin; GAO Haoyang; LI Chenhui; ZHANG Xitong; SU Chenghai

doi:10.11993/j.issn.2096-3920.2025-0171

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MA Junli, WANG Shuai, LIU Wenbin, GAO Haoyang, LI Chenhui, ZHANG Xitong, SU Chenghai. Design and Application of Vehicle Mounted Industrial CT Nondestructive Testing System and Underwater Unmanned Equipment Defect Recognition Software[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2025-0171

Citation:

MA Junli, WANG Shuai, LIU Wenbin, GAO Haoyang, LI Chenhui, ZHANG Xitong, SU Chenghai. Design and Application of Vehicle Mounted Industrial CT Nondestructive Testing System and Underwater Unmanned Equipment Defect Recognition Software[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2025-0171

Citation:

MA Junli, WANG Shuai, LIU Wenbin, GAO Haoyang, LI Chenhui, ZHANG Xitong, SU Chenghai. Design and Application of Vehicle Mounted Industrial CT Nondestructive Testing System and Underwater Unmanned Equipment Defect Recognition Software[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2025-0171

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Design and Application of Vehicle Mounted Industrial CT Nondestructive Testing System and Underwater Unmanned Equipment Defect Recognition Software

doi: 10.11993/j.issn.2096-3920.2025-0171

1.
The 705 Research Institute, China Shipbuilding Industry Corporation Limited, Xi’an 710072, China
2.
Unit 92840S1, The People’s Liberation Army of China, Qingdao 266000, China

Received Date: 2025-12-22
Accepted Date: 2026-03-04
Rev Recd Date: 2026-03-02

Available Online: 2026-07-17

Abstract

Abstract

To satisfy the inspection demands of unmanned undersea equipment that are stored in remote areas, unsuitable for frequent transportation and difficult to conduct centralized testing, this paper builds a vehicle-mounted industrial computed tomography(CT) nondestructive defect detection and identification system with hardware subsystems including power supply, X-ray source, detection, acquisition and transmission. Combined with deep neural networks, an image recognition software for defects in industrial CT inspection products is developed to process inspection images and realize the identification and marking of internal product defects. Practical test results demonstrate that the proposed system can effectively detect and identify pores, cracks, debonding and other internal defects in high-energy fillers of unmanned undersea equipment. The imaging accuracy exceeds 98%, and both the missed detection rate and false alarm rate of defect identification are no more than 5%. This system can adapt to different materials and shapes of the products, and can operate in various environments. It has a promising application prospect.
- vehicle-mounted industrial CT,
- unmanned undersea equipment,
- operation and maintenance support,
- nondestructive testing,
- image processing,
- defect identification

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References(38)

References

[1]	哈海荣, 王团盟, 鲁忠宝, 等. 一种战斗部用DNAN基炸药热塑态装药改进工艺[J]. 水下无人系统学报, 2020, 28(2): 209-213, 230. Ha H R, Wang T M, Lu Z B, et al. Improved thermoplastic charge technique of DNAN-based explosive for warhead[J]. Journal of Unmanned Undersea Systems, 2020, 28(2): 209-213, 230.
[2]	鲁忠宝, 黎勤, 马军利, 等. 鱼雷战斗部装药特点与发展[J]. 水下无人系统学报, 2018, 26(1): 10-15, 45. Lu Z B, Li Q, Ma J L, et al. Research on charge in torpedo warhead, journal of unmanned undersea systems[J]. 2018, 26(1): 10-15, 4.
[3]	崔宝成. 浅析医学影像技术学-CT[J]. 世界最新医学信息文摘, 2015, 15(72): 111-112.
[4]	Xiao Y S, Chen Z Q, Yu D W, et al. The applications of industrial CT NDT technology in geological research[C]//proceedings of the 19th World Conference on Non-Destructive Testing, 2016.
[5]	唐庆明, 张明. 固体火箭发动机的装药缺陷及检测方法[J]. 飞航导弹, 2006(7): 52-54. doi: 10.3969/j.issn.1009-1300.2010.01.016
[6]	汤戈, 赵欣雨, 王宇翔, 等. 工业CT技术在地球科学中的应用[J]. CT理论与应用研究, 2024, 33(1): 119-134. Tang G, Zhao X Y, Wang Y X, et al. Applications of industrial computed tomography technology in the geosciences[J]. CT Theory and Applications, 2024, 33(1): 119-134.
[7]	Ewert U, Fuchs T. Progress in digital industrial radiology. Pt. 2, Computed tomography(CT)[J]. Badania nieniszczące idiagnostyka, 2017(1-2): 7-14.
[8]	Barciewicz M, Ryniewicz A. The application of computed tomography in the automotive world–how industrial CT works[J]. Technical Transactions, 2018(115): 181-188. doi: 10.4467/2353737xct.18.141.8980
[9]	宁亚倩. 基于X射线CT成像技术的芒果内部缺陷判别与品质检测研究[D]. 武汉: 武汉轻工大学, 2024.
[10]	De Chiffre L, Carmignato S, Kruth J P, et al. Industrial applications of computed tomography[J]. CIRP annals, 2014, 63(2): 655-677. doi: 10.1016/j.cirp.2014.05.011
[11]	Hu S, Xu J K, Lv M C, et al. The Application of Industrial CT Detection Technology in Defects inspection of lithium Ion Battery[J]. Journal of Physics: Conference Series, 2021, 2083(3): 32075. doi: 10.1088/1742-6596/2083/3/032075
[12]	唐盛明, 齐子诚, 刘子瑜, 等. 电子束焊缝超声波C扫描与工业CT检测方法测试结果比较[J]. 无损检测, 2014, 36(10): 49-52,83. Tang S M, Qi Z C, Liu Z Y, at al. Comparison of test method between ultrasonic C-scan and industrial CT in electron beam welding testing[J]. Nondestructive Testing, 2014, 36(10): 49-52,83.
[13]	唐盛明, 齐子诚, 郑颖, 等. 工业CT在混凝土钢筋腐蚀检测中的应用[J]. 无损检测, 2017, 39(12): 10-14. Tang S M, Qi Z C, Zheng Y, at al. Application of industrial ct in corrosion detection of concrete reinforcement[J]. Nondestructive Testing, 2017, 39(12): 10-14.
[14]	Gao Y L, Chen X Q, Liu Y P, et al. Effect of warhead wall thickness on charge uniformityin industrial CT detection[J]. Journal of Measurement Science and Instrumentation, 2017, 8(1): 9-16.
[15]	吕延达, 陈亦维, 郭洪勤, 等. 工业CT检测技术在5 kN发动机研制中的应用[J]. 宇航材料工艺, 2020, 50(4): 87-91. doi: 10.12044/j.issn.1007-2330.2020.04.017 Lü Y D, Chen Y W, Guo H Q, et al. Application of industrial CT detectior technology in defect inspection of 5 kN engine[J]. Aerospace Materials & Technology, 2020, 50(4): 87-91. doi: 10.12044/j.issn.1007-2330.2020.04.017
[16]	温银堂, 张松, 张玉燕, 等. 新型复合材料界面粘接缺陷的CT检测及表征[J]. 中国测试, 2020, 46(1): 12-17. Wen Y T, Zhang S, Zhang Y Y, et al. CT detection and characterization for interface defects of new composite materials[J]. China Measurement & Test, 2020, 46(1): 12-17.
[17]	李智, 陈思宇. 采用X射线CT技术评价沥青路面结构内部质量均匀性[J]. 公路交通科技, 2014, 31(12): 6-11. Li Z, Chen S Y. Evaluating homogeneity of interval quality of asphalt pavement structure with X-ray computed tomography[J]. Journal of Highway and Transportation Research and Development, 2014, 31(12): 6-11.
[18]	宋婧, 杨泉. 航天火工产品无损检测技术的应用与发展[J]. 现代制造技术与装备, 2020(6): 164-165.
[19]	涂旺, 王文强, 陈佳慧, 等. 增材制造小缺陷的显微CT检测[J]. 无损检测, 2024, 46(5): 50-55,61. Tu W, Wang W Q, Chen J H, et al. Micro-CT detection of small defects in additive manufacturing[J]. Nondestructive Testing, 2024, 46(5): 50-55,61.
[20]	孟嘉, 肖鹏. 一种金属增材结构中微缺陷的工业CT检测灵敏度验证方法[J]. 宇航材料工艺, 2024, 54(6): 91-97. doi: 10.12044/j.issn.1007-2330.2024.06.013 Meng J, Xiao P. A sensitivity verification method of industrial CT detection for micro-defects in metal additive manufacturing structures[J]. Aerospace Materials & Technology, 2024, 54(6): 91-97. doi: 10.12044/j.issn.1007-2330.2024.06.013
[21]	Zuo Z P, Sun T. Application of industrial CT technology in additive manufacturing field[C]// 2022 Photonics & Electromagnetics Research Symposium(PIERS), 2022: 197-203.
[22]	Wei Z H, Liu B D, Dong B, et al. A joint reconstruction and segmentation method for limited-angle X-Ray tomography[J]. IEEE Access, 2018, 6: 7780-7791. doi: 10.1109/ACCESS.2018.2800719
[23]	Fang Z, Wang T. Novel design of industrial real-time CT system based on sparse-view reconstruction and deep-learning image enhancement[J]. Electronics, 2023, 12(8): 1815. doi: 10.3390/electronics12081815
[24]	阳庆国, 谭伯仲. 高性能锥束工业X射线CT系统研制与应用[J]. 光学精密工程, 2023, 31(6): 804-812. doi: 10.37188/OPE.20233106.0804 Yang Q G, Tan B Z. Development and application of high-performance cone-beam industrial X-ray CT system[J]. Optics and Precision Engineering, 2023, 31(6): 804-812. doi: 10.37188/OPE.20233106.0804
[25]	吕宁, 崔庆忠, 黄学义, 等. 基于维纳滤波的装药孔隙工业CT图像恢复方法[J]. 科学技术与工程, 2015, 15(30): 132-138. doi: 10.3969/j.issn.1671-1815.2015.30.025 Lü N, Cui Q Z, Huang X Y, et al. An industrial CT image restoration method for explosive cavity based on Wiener filtering[J]. Science Technology and Engineering, 2015, 15(30): 132-138. doi: 10.3969/j.issn.1671-1815.2015.30.025
[26]	Singh G, Kaur M, Jindal P K, et al. Convolution neural network(CNN) layers in deep learning: A review[J]. AIP Conference Proceedings, 2024, 3121: 40033. doi: 10.1063/5.0221488
[27]	Hu F Y, Wan X J, Shen M F, et al. Survey progress on image instance segmentation methods of deep convolutional neural network[J]. Computer Science, 2022, 49(5): 10-24.
[28]	魏龙, 刘乐, 刘吉吉, 等. 基于机器学习的固体火箭发动机无损检测数据智能判读[J]. 国防科技, 2021, 42(4): 69-75. doi: 10.13943/j.issn1671-4547.2021.04.12 Wei L, Liu L, Liu J J, et al. Intelligent interpretation of non-destructive testing data for solid rocket engines based on machine learning[J]. National Defence Technology, 2021, 42(4): 69-75. doi: 10.13943/j.issn1671-4547.2021.04.12
[29]	常海涛. 基于Faster R-CNN的工业CT图像缺陷检测研究[D]. 兰州: 兰州交通大学, 2018.
[30]	张磊, 陈研, 邱焓, 等. 一种车载X射线工业CT系统[J]. 无损探伤, 2021, 45(1): 32-37. Zhang L, Chen Y, Qiu H, et al. Vehicle-mounted X-ray industrial CT system[J]. Nondestructive Testing Technology, 2021, 45(1): 32-37.
[31]	杨建学. 一款车载式检查系统的研究开发[J]. 中国机械, 2023(10): 2-8.
[32]	李晓晴. 车载式高精度工业CT结构参数快速获取研究[D]. 北京: 清华大学, 2018.
[33]	Li M, Zhang Z J, Lei L P, et al. Agricultural greenhouses detection in high-resolution satellite images based on convolutional neural networks: Comparison of faster R-CNN, YOLO v3 and SSD[J]. Sensors, 2020, 20(17): 4938. doi: 10.3390/s20174938
[34]	Murat A A, Kiran M S. A comprehensive review on YOLO versions for object detection[J]. Engineering Science and Technology, an International Journal, 2025, 70: 102161. doi: 10.1016/j.jestch.2025.102161
[35]	Lv Z L, Zhao Z Q, Xia K W, et al. Steel surface defect detection based on MobileViTv2 and YOLOv8[J]. The Journal of Supercomputing, 2024, 80(13): 18919-18941. doi: 10.1007/s11227-024-06248-w
[36]	纪泳丞, 李毅, 陈汉平, 等. 基于改进YOLOv11的轻量化裂缝检测算法[J/OL]. 华南理工大学学报(自然科学版), 2026-2-12. http://link.cnki.net/urlid/44.1251.T.20260211.1341.002.
[37]	Alam M S, Alam M, Tufail M, et al. TobSet: A new tobacco crop and weeds image dataset and its utilization for vision-based spraying by agricultural robots[J]. Applied Sciences, 2022, 12(3): 1308. doi: 10.3390/app12031308
[38]	Ocholla I A, Pellikka P, Karanja F, et al. Livestock detection and counting in Kenyan rangelands using aerial imagery and deep learning techniques[J]. Remote Sensing, 2024, 16(16): 2929. doi: 10.3390/rs16162929