Abstract
Object detection is used to locate the occurrence of every object and analyze their important features. Detection over the land is far easier, and the results are more accurate. Still, when it comes to underwater object detection, it becomes more challenging due to the lack of visibility under the water. For better detection of underwater sea cucumbers, sea urchins, and scallops based on You Look Only Once version 7 (YOLOv7), it has been stated that among all of the real-time object detectors, this one shows 66.8% AP and surpasses all other detectors regarding speed. The detection outcomes exhibit a noteworthy improvement in the object detection and benefits in the further upcoming research works related to the deep study of underwater environment and exploration of unexplored, with the idea of the accuracy of YOLOv7 and comparison with other detection methods like YOLOv7-X, YOLOv7-E6, and YOLOv7-D6, which are generated by increasing the scaling of width and length.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Juan, L.I., Chen, X., Jiang, L., **ao, Y.: Detection and Analysis of Behavior Trajectory for Sea Cucumbers Based on Deep Learning. IEEE (2019)
Basuel, R., Fajardo, A., Perez, N.: Identification of Dried Sea Cucumber Species Using Color and Texture Extraction, pp. 148–153 (2022). https://doi.org/10.1109/ICITACEE55701.2022.9924102. **a, C., Fu, L., Liu, H.: In situ sea cucumber detection based on deep learning approach
Ma, K., Huang, B., Yin, H.: Underwater Sea Cucumbers Detection Based on Improved SSD, pp. 343–347 (2019). https://doi.org/10.1109/ICPICS47731.2019.8942503
Han, F., et al.: Underwater image processing and object detection based on deep CNN method. J. Sens., 1–20 (2020). https://doi.org/10.1155/2020/6707328
Kaiyan, Z., **ang, L., Weibo, S.: Underwater Object Detection Using Transfer Learning with Deep Learning, pp. 157–160 (2020). https://doi.org/10.1145/3419635.3419678
Yu, Y., Zhao, J., Gong, Q., Huang, C., Zheng, G., Ma, J.: Real-time underwater maritime object detection in side-scan sonar images based on transformer-YOLOv5. Remote Sens. 13, 3555 (2021). https://doi.org/10.3390/rs13183555
Salman, A., Siddiqui, S., Shafait, F., Mian, A., Shortis, M., Khurshid, K., Ulges, A., Schwanecke, U.: Automatic fish detection in underwater videos by a deep neural network-based hybrid motion learning system. ICES J. Mar. Sci. 77 (2019). https://doi.org/10.1093/icesjms/fsz025
Kim, B., Yu, S.-C.: Imaging sonar based real-time underwater object detection utilizing AdaBoost method. In: 2017 IEEE Underwater Technology (UT), pp. 1–5, Busan, Korea (South) (2017). https://doi.org/10.1109/UT.2017.7890300. Cui, S., Zhou, Y., Wang, Y., Zhai, L: Fish detection using deep learning. Published on 23 Jan 2020
Cui, S., Zhou, Y., Wang, Y., Zhai, L.: Fish detection using deep learning. Appl. Comput. Intell. Soft Comput. 2020, 1–13 (2020). https://doi.org/10.1155/2020/3738108. Chuang, M.-C., Hwang, J.-N.: A feature learning and object recognition framework for underwater fish images
Sun, B., Zhu, D.: Underwater target detection based on Faster R-CNN and adversarial occlusion network. Eng. Appl. Artif. Intell. 100, 104190 (2021)
Chuang, M.-C., Hwang, J.-N., Williams, K.: A feature learning and object recognition framework for underwater fish images. IEEE Trans. Image Process. 25, 1–1 (2016). https://doi.org/10.1109/TIP.2016.2535342
Samiappan, D., Latha, S., Rama Rao, T., Verma, D., Sriharsha, C.S.A.: Enhancing machine learning aptitude using significant cluster identification for augmented image refining. Int. J. Pattern Recognit. Artif. Intell. 34(09), 2051009 (2020). https://doi.org/10.1142/S021800142051009X
Mathias, A., Narayanamoorthi, R., Dhanalakshmi, S.: Tracking of underwater objects with occlusion awareness using an adaptive DEEP SORT and GMM approach. In: OCEANS 2022 – Chennai, pp. 1–5 (2022). https://doi.org/10.1109/OCEANSChennai45887.2022.9775470
Yeoh, P.S.Q., Lai, K.W., Goh, S.L., Hasikin, K., Hum, Y.C., Tee, Y.K., Dhanalakshmi, S.: Emergence of deep learning in knee osteoarthritis diagnosis. Comput. Intell. Neurosci. 2021, 4931437 (2021). https://doi.org/10.1155/2021/4931437. PMID: 34804143; PMCID: PMC8598325
Barui, S., Latha, S., Samiappan, D., Muthu, P.: SVM pixel classification on colour image segmentation. J. Phys. Conf. Ser. 1000(1), 012110 (2018). IOP Publishing
Latha, S., Samiappan, D.: Despeckling of carotid artery ultrasound images with a calculus approach. Curr. Med. Imaging. 15(4), 414–426 (2019)
Mathias, A., Dhanalakshmi, S., Kumar, R., Narayanamoorthi, R.: Underwater object detection based on bi-dimensional empirical mode decomposition and Gaussian Mixture Model approach. Eco. Inform. 66, 101469 (2021). https://doi.org/10.1016/j.ecoinf.2021.101469
Dhanalakshmi, S., Venkatesh, C.: Classification of ultrasound carotid artery images using texture features. Int. Rev. Comput. Softw. (IRECOS). 8(4), 933–940 (2013)
Monika, R., Dhanalakshmi, S., Sreejith, S.: Coefficient random permutation based compressed sensing for medical image compression. In: Advances in Electronics, Communication and Computing, pp. 529–536. Springer, Singapore (2018)
Ancuti, C.O., Ancuti, C., De Vleeschouwer, C., Bekaert, P.: Color balance and fusion for underwater image enhancement. IEEE Trans. Image Process. 27(1), 379–393 (2018). https://doi.org/10.1109/TIP.2017.2759252
Li, C., et al.: An underwater image enhancement benchmark dataset and beyond. IEEE Trans. Image Process. 29, 4376–4389 (2020). https://doi.org/10.1109/TIP.2019.2955241
Qian, Y., Zheng, H., He, D., Zhang, Z., Zhang, Z.: R-CNN object detection inference with deep learning accelerator. In: 2018 IEEE/CIC International Conference on Communications in China (ICCC Workshops), pp. 297–302, Bei**g, China (2018). https://doi.org/10.1109/ICCChinaW.2018.8674519
Girshick, R.: Fast R-CNN. In: 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1440–1448, Santiago, Chile (2015). https://doi.org/10.1109/ICCV.2015.169
Hmidani, O., Ismaili Alaoui, E.M.: A comprehensive survey of the R-CNN family for object detection. In: 2022 5th International Conference on Advanced Communication Technologies and Networking (CommNet), pp. 1–6, Marrakech, Morocco (2022). https://doi.org/10.1109/CommNet56067.2022.9993862
Wang, C., Peng, Z.: Design and implementation of an object detection system using faster R-CNN. In: 2019 International Conference on Robots & Intelligent System (ICRIS), pp. 204–206, Haikou, China (2019). https://doi.org/10.1109/ICRIS.2019.00060
Gavrilescu, R., et al.: Faster R-CNN: an approach to real-time object detection. In: 2018 International Conference and Exposition on Electrical and Power Engineering (EPE), Iasi, Romania (2018)
Liu, C., Wang, Z., Wang, S., Tang, T., Tao, Y., Yang, C., Li, H., Liu, X., Fan, X.: A new dataset, Poisson GAN and AquaNet for underwater object grabbing. IEEE Trans. Circuits Syst. Video Technol. 32(5), 2831–2844 (2021). https://doi.org/10.48550/ar**v.2003.01446
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Srivastava, U., Balaji, A., Yogesh, S., Kalyaan, C.K., Narayanamoorthi, R., Dhanalakshmi, S. (2024). Detection of Echinoderms Underwater Using Deep Learning Network. In: Gopi, E.S., Maheswaran, P. (eds) Proceedings of the International Conference on Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication. MDCWC 2023. Signals and Communication Technology. Springer, Cham. https://doi.org/10.1007/978-3-031-47942-7_32
Download citation
DOI: https://doi.org/10.1007/978-3-031-47942-7_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-47941-0
Online ISBN: 978-3-031-47942-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)