A Method of Erect Rail Barricade Recognition Based on Forward-Looking 3D Sonar

LI Bao-qi; REN Lu-lu; CHEN Fa; QIAN Bin; HUANG Hai-ning

doi:10.11993/j.issn.2096-3920.2022-0016

Volume 30 Issue 6

Dec 2022

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Article Navigation > Journal of Unmanned Undersea Systems > 2022 > 30(6): 747-753

LI Bao-qi, REN Lu-lu, CHEN Fa, QIAN Bin, HUANG Hai-ning. A Method of Erect Rail Barricade Recognition Based on Forward-Looking 3D Sonar[J]. Journal of Unmanned Undersea Systems, 2022, 30(6): 747-753. doi: 10.11993/j.issn.2096-3920.2022-0016

Citation:

LI Bao-qi, REN Lu-lu, CHEN Fa, QIAN Bin, HUANG Hai-ning. A Method of Erect Rail Barricade Recognition Based on Forward-Looking 3D Sonar[J]. Journal of Unmanned Undersea Systems, 2022, 30(6): 747-753. doi: 10.11993/j.issn.2096-3920.2022-0016

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A Method of Erect Rail Barricade Recognition Based on Forward-Looking 3D Sonar

doi: 10.11993/j.issn.2096-3920.2022-0016

LI Bao-qi^{1, 4
,},
REN Lu-lu^{1, 3, 4},
CHEN Fa²,
QIAN Bin²,
HUANG Hai-ning^{1, 4}

1.
Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China
2.
Suzhou Soundtech Oceanic Instrument Ltd., Suzhou 215000, China
3.
University of Chinese Academy of Sciences, Beijing 100190, China
4. Key Laboratory of Science and Technology on Advanced Underwater Acoustic Signal Processing, Chinese Academy of Sciences, Beijing 100190, China

Received Date: 2022-07-20
Accepted Date: 2022-11-02
Rev Recd Date: 2022-09-20

Available Online: 2022-11-07

Abstract

Abstract

To alleviate the difficulty in detecting and recognizing erect rail barricades, a forward-looking three-dimensional(3D) imaging sonar is used to improve the effectiveness of erect rail barricade recognition, and a single shot detector(SSD)-based 3D point cloud(PCSSD) target recognition method is proposed. First, the original beam domain data are processed using a threshold filter and pass-through filter. Next, forward projection of the filtered 3D point cloud data is performed, and the grayscale depth image and pseudo color depth image are obtained. The SSD was then used to recognize the targets from the pseudo color depth image. Subsequently, the depth range of the selected targets is calculated based on the characteristics of the targets recognized from the grayscale depth image. Finally, erect rail barricades in the form of 3D point clouds were detected by combining the two-dimensional detection results and the corresponding depth range. A feature extraction block based on the multi-scale attention mechanism was proposed, and a novel target detector SSD-MV3ME was used for the design. On the 3D point cloud erect rail barricades dataset GTZ, under the condition of the same detection time, the detection accuracy of SSD-MV3ME was higher than that of the lightweight target detection model SSD-MV3 by 1.05%, and the model parameters had less than 2 482 kB. The results show that the PCSSD based on SSD-MV3ME is more suitable for erect rail barricade recognition tasks.
- forward-looking three-dimensional sonar,
- beamforming,
- sonar image,
- target detection,
- attention mechanism

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References(19)

References

[1]	鲍小恒, 堵永国, 白书欣. 一种新型轨条砦灵巧破障炸弹的设计[J]. 工兵装备研究, 2006, 25(1): 5-8. Bao Xiao-heng, Du Yong-guo, Bai Shu-xin. Design on a New Smart Breaching Bomb for Rail Obstacles[J]. Engineer Equipment Research, 2006, 25(1): 5-8.
[2]	杨子庆, 张婉. 外军登陆作战上陆阶段工程保障装备的现状与特点[J]. 工兵装备研究, 2006, 25(1): 56-59. Yang Zi-qing, Zhang Wan. Status Quo and Characteristics of Foreign Engineer Support Equipment during Assault Landing Operation[J]. Engineer Equipment Research, 2006, 25(1): 56-59.
[3]	张少光, 孙严峰, 蒋洪涛. 台军抗登陆地雷障碍物的发展与研究[J]. 工兵装备研究, 2007, 26(4): 57-60. Zhang Shao-guang, Sun Yan-feng, Jiang Hong-tao. Development and Research on Anti-landing Mine Obstacles of Taiwan Army[J]. Engineer Equipment Research, 2007, 26(4): 57-60.
[4]	冷画屏, 周世华, 肖利辉. 两栖登陆部队指挥信息系统建设研究[J]. 指挥控制与仿真, 2015, 37(1): 11-14. Leng Hua-ping, Zhou Shi-hua, Xiao Li-hui. Research on Construction of Amphibious Landing Force Command Information System[J]. Command Control & Simulation, 2015, 37(1): 11-14.
[5]	程亚楠, 刘晓东, 张东升. 基元扰动对三维前视声纳测向性能的影响分析[J]. 网络新媒体技术, 2020, 9(5): 15-24. doi: 10.3969/j.issn.2095-347X.2020.05.003 Cheng Ya-nan, Liu Xiao-dong, Zhang Dong-sheng, et al. Influence of Sensor Uncertainty on the Direction Estimation of 3D Forward-looking Sonar[J]. Journal of Network New Media, 2020, 9(5): 15-24. doi: 10.3969/j.issn.2095-347X.2020.05.003
[6]	Yann L C, Bengio Y, Hinton G. Deep Learning[J]. Nature, 2015, 521(7553): 436-444. doi: 10.1038/nature14539
[7]	Kwok R. Deep Learning Powers a Motion-Tracking Revolution[J]. Nature, 2019, 574(7776): 137-138. doi: 10.1038/d41586-019-02942-5
[8]	Zhou Y, Tuzel O. Voxelnet: End-to-end Learning for Point Cloud Based 3d Object Detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE, 2018: 4490-4499.
[9]	Yan Y, Mao Y, Li B. Second: Sparsely Embedded Convolutional Detection[J]. Sensors, 2018, 18(10): 3337. doi: 10.3390/s18103337
[10]	Lang A H, Vora S, Caesar H, et al. Pointpillars: Fast Encoders for Object Detection from Point Clouds[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach, USA: IEEE, 2019: 12697-12705.
[11]	Yang Z, Sun Y, Liu S, et al. 3DSSD: Point-based 3d Single Stage Object Detector[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE, 2020: 11040-11048.
[12]	Redmon J, Divvala S, Girshick R, et al. You Only Look Once: Unified, Real-time Object Detection[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA: IEEE, 2016: 779-788.
[13]	Ren S Q, He K M, Girshick R, et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149. doi: 10.1109/TPAMI.2016.2577031
[14]	Liu W, Anguelov D, Erhan D, et al. SSD: Single Shot Multibox Detector[C]//European Conference on Computer Vision. Cham: Springer, 2016: 21-37.
[15]	Haward A G, Zhu M L, Chen B, et al. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications[J/OL]. ArXiv Preprint (2017-04-17)[2022-09-22]. https://arxiv.org/abs/1704.04861.
[16]	Sandler M, Howard A, Zhu M, et al. Mobilenetv2: Inverted Residuals and Linear Bottlenecks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE, 2018: 4510-4520.
[17]	Hu J, Shen L, Sun G. Squeeze-and-excitation Networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA: IEEE, 2018: 7132-7141.
[18]	Howard A, Sandler M, Chu G, et al. Searching for Mobilenetv3[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. Long Beach, USA: IEEE, 2019: 1314-1324.
[19]	Wang Q, Wu B, Zhu P, et al. ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle, USA: IEEE, 2020.