Researching | Lightweight YOLOv5 sonar image object detection algorithm and implementation based on ZYNQ

Journals >Opto-Electronic Engineering >Volume 51 >Issue 1 >Page 230284-1 > Article

Opto-Electronic Engineering
Vol. 51, Issue 1, 230284-1 (2024)

Lightweight YOLOv5 sonar image object detection algorithm and implementation based on ZYNQ

Dongdong Zhao¹, Dunhan Xie¹, Peng Chen^1、*, Ronghua Liang¹, Yi Shen¹, and Xinxin Guo²

Author Affiliations

¹College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang 310023, China

²Institute of Deep-sea Science and Engineering, Chinese Academy of Sciences, Sanya, Hainan 572000, China

show less

DOI: 10.12086/oee.2024.230284 Cite this Article

Dongdong Zhao, Dunhan Xie, Peng Chen, Ronghua Liang, Yi Shen, Xinxin Guo. Lightweight YOLOv5 sonar image object detection algorithm and implementation based on ZYNQ[J]. Opto-Electronic Engineering, 2024, 51(1): 230284-1 Copy Citation Text

show less

References

[1] G Guo, X K Wang, H P Xu. Review on underwater target detection, recognition and tracking based on sonar image. Control Decis, 33, 906-922(2018).

[2] J Petrich, M F Brown, J L Pentzer et al. Side scan sonar based self-localization for small autonomous underwater vehicles. Ocean Eng, 161, 221-226(2018).

[3] S W Huang, E Chen, J Guo. Efficient seafloor classification and submarine cable route design using an autonomous underwater vehicle. IEEE J Oceanic Eng, 43, 7-18(2018).

[4] C Park, Y Kim, H Lee et al. Development of a 2 MHz sonar sensor for inspection of bridge substructures. Sensors (Basel), 18, 1222(2018).

[5] P Chen, X W Cai, D D Zhao et al. Despeckling for side-scan sonar images based on adaptive block-matching and 3D filtering. Opto-Electron Eng, 47, 190580(2020).

[6] A Abu, R Diamant. A statistically-based method for the detection of underwater objects in sonar imagery. IEEE Sensors J, 19, 6858-6871(2019).

[7] S Negahdaripour. Application of forward-scan sonar stereo for 3-D scene reconstruction. IEEE J Oceanic Eng, 45, 547-562(2020).

[8] Z G Shang, C H Zhao, J Wan. Application of multi-resolution analysis in sonar image denoising. J Sys Eng Electron, 19, 1082-1089(2008).

[9] Y Jin, B Ku, J Ahn et al. Nonhomogeneous noise removal from side-scan sonar images using structural sparsity. IEEE Geosci Remote Sens Lett, 16, 1215-1219(2019).

[10] Z Wang, S W Zhang, W Z Huang et al. Sonar image target detection based on adaptive global feature enhancement network. IEEE Sensors J, 22, 1509-1530(2022).

[11] M Mignotte, C Collet, P Pérez et al. Markov random field and fuzzy logic modeling in sonar imagery: application to the classification of underwater floor. Comput Vis Image Underst, 79, 4-24(2000).

[12] D Köhntopp, B Lehmann, D Kraus et al. Classification and localization of naval mines with superellipse active contours. IEEE J Ocean Eng, 44, 767-782(2019).

[13] M Valdenegro-Toro. End-to-end object detection and recognition in forward-looking sonar images with convolutional neural networks, 144-150(2016). https://doi.org/10.1109/AUV.2016.7778662

[14] D P Williams. Underwater target classification in synthetic aperture sonar imagery using deep convolutional neural networks, 2497-2502(2016). https://doi.org/10.1109/ICPR.2016.7900011

[15] J Kim, S C Yu. Convolutional neural network-based real-time ROV detection using forward-looking sonar image, 396-400(2016). https://doi.org/10.1109/AUV.2016.7778702

[16] G X Liu, S Y Liu, J F Wu et al. Machine vision object detection algorithm based on deep learning and application in banknote detection. China Measur Test Technol, 45, 1-9(2019).

[17] J Harikrishnan, A Sudarsan, A Sadashiv et al. Vision-face recognition attendance monitoring system for surveillance using deep learning technology and computer vision, 1-5(2019). https://doi.org/10.1109/ViTECoN.2019.8899418

[18] B Q Li, H N Huang, J Y Liu et al. Synthetic aperture sonar underwater multi-scale target efficient detection model based on improved single shot detector. J Electron Inf Technol, 43, 2854-2862(2021).

[19] L Chen, J L Zhang, H Peng et al. Few-shot image classification via multi-scale attention and domain adaptation. Opto-Electron Eng, 50, 220232(2023).

[20] X Y Ge, N Y Wei, H K Zhou et al. Research on small underwater target detection technology based on side-scan sonar. Digit Ocean Underwater Warf, 6, 155-161(2023).

[21] A Bochkovskiy, C Y Wang, H Y M Liao. YOLOv4: optimal speed and accuracy of object detection(2020). https://doi.org/10.48550/arXiv.2004.10934

[22] A Singh, V Kalaichelvi, R Karthikeyan. Performance analysis of object detection algorithms for robotic welding applications in planar environment. Int J Comput Integr Manuf, 36, 1083-1108(2023).

[23] X Chen, D L Peng, Y Gu. Real-time object detection for UAV images based on improved YOLOv5s. Opto-Electron Eng, 49, 210372(2022).

[24] Z Ge, S T Liu, F Wang et al. YOLOX: exceeding YOLO series in 2021(2021). https://doi.org/10.48550/arXiv.2107.08430

[25] B Y Ying, Y C Xu, S Zhang et al. Weed detection in images of carrot fields based on improved YOLO v4. Traitem Signal, 38, 341-348(2021).

[26] L Huang, Y Yang, C Y Yang et al. FS-YOLOv5: lightweight infrared rode target detection method. Comput Eng Appl, 59, 215-224(2023).

[27] H L Li, J Li, H B Wei et al. Slim-neck by GSConv: a better design paradigm of detector architectures for autonomous vehicles(2022). https://doi.org/10.48550/arXiv.2206.02424

[28] Z Q Feng, Z J Xie, Z W Bao et al. Real-time dense small object detection algorithm for UAV based on improved YOLOv5. Acta Aeronaut Astronaut Sin, 44, 327106(2023).

[29] X K Zhu, S C Lyu, X Wang et al. TPH-YOLOv5: improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios, 2778-2788(2021). https://doi.org/10.1109/ICCVW54120.2021.00312

[30] Z Y Niu, G Q Zhong, H Yu. A review on the attention mechanism of deep learning. Neurocomputing, 452, 48-62(2021).

[31] J Hu, L Shen, G Sun. Squeeze-and-excitation networks, 7132-7141(2018). https://doi.org/10.1109/CVPR.2018.00745

[32] S Woo, J Park, J Y Lee et al. CBAM: convolutional block attention module, 3-19(2018). https://doi.org/10.1007/978-3-030-01234-2_1

[33] L X Yang, R Y Zhang, L D Li et al. SimAM: a simple, parameter-free attention module for convolutional neural networks, 11863-11874(2021).

[34] Z H Zheng, P Wang, W Liu et al. Distance-IoU loss: faster and better learning for bounding box regression, 12993-13000(2020). https://doi.org/10.1609/aaai.v34i07.6999

[35] M Y Yuan, Y L Song, C Zhang et al. Underwater object detection based on GA-RetinaNet. Comput Syst Appl, 32, 80-90(2023).

[36] T Y Lin, P Goyal, R Girshick et al. Focal loss for dense object detection, 2999-3007(2017). https://doi.org/10.1109/ICCV.2017.324

Get PDF(in Chinese)

Figures&Tables (15)

References (36)

Copy Citation Text

Dongdong Zhao, Dunhan Xie, Peng Chen, Ronghua Liang, Yi Shen, Xinxin Guo. Lightweight YOLOv5 sonar image object detection algorithm and implementation based on ZYNQ[J]. Opto-Electronic Engineering, 2024, 51(1): 230284-1

Download Citation

Save the article for my favorites

Paper Information

Recommended Topics

laser devices and laser physics

Lasers and Laser Optics

laser manufacturing

Instrumentation, Measurement and Metrology

Set citation alerts for the article

Please enter your email address