Abstract
The exploration of the underwater environment has become increasingly important due to the utilization and development of deep-sea resources in recent years. To overcome the hazards of high-pressure deep-sea conditions, autonomous underwater operations have become essential, with intelligent computer vision playing a pivotal role.This study proposes a novel deep-learning model for the effective detection and classification of underwater object images (UWOI). The model addresses the challenge of low-quality, weak illumination, and noise in underwater images by employing an Anisotropic Diffusion Filter (ADF) during pre-processing. To enhance segmentation accuracy, the model utilizes Adaptive Spectral Clustering (ASC). Textural and statistical features are then extracted using the Gray Level Co-occurrence Matrix (GLCM) for robust feature representation. Finally, the proposed model leverages an Improved Dung Beetle Optimization (IDBO) algorithm in conjunction with a Recurrent Convolutional Neural Network (RCNN) for UWOI detection and classification. Extensive evaluations demonstrate that the proposed model achieves significantly improved performance compared to previous methods, attaining superior results in terms of accuracy, Dice score, sensitivity, Structural Similarity Index (SSIM), and specificity. The proposed method consistently demonstrates strong performance in both specificity and sensitivity compared to existing methods, with specificity ranging from 94 to 97% across iterations (10–100), exceeding existing methods (SDCS, UCPS, AEA-QoS, and RLOD), and sensitivity ranging from 94% to 96.65% across iterations (10–50), with the value rising to 96.65% at the 100th iteration. Overall, the findings suggest that the proposed method achieves both high true positive rates (specificity) and low false negative rates (sensitivity), indicating its effectiveness in correctly identifying true targets and minimizing false alarms compared to existing methods. This work contributes to the advancement of underwater object recognition by offering a robust and efficient deep-learning approach.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
No datasets were generated or analysed during the current study.
References
Aggarwal AK (2022) Learning texture features from GLCM for classification of brain tumor MRI images using random forest classifier. Trans Signal Process 18:60–63
Akkara S, Jarin T (2022) Pi controller based switching reluctance motor drives using smart bacterial foraging algorithm. EAI Endorsed Trans on AI Robot 1(1)
Berahmand K, Mohammadi M, Faroughi A, Mohammadiani RP (2022) A novel method of spectral clustering in attributed networks by constructing parameter-free affinity matrix. Cluster Comp 1–20
Diwan T, Anirudh G, Tembhurne JV (2023) Object detection using YOLO: Challenges, architectural successors, datasets and applications. Multimed Tools Appl 82(6):9243–9275
Du B, Mao D, Wang Z, Qiu Z, Yan H, Feng K, Zhang Z (2021) Mapping wetland plant communities using unmanned aerial vehicle hyperspectral imagery by comparing object/pixel-based classifications combining multiple machine-learning algorithms. IEEE J Sel Top Appl Earth Observations Remote Sensing 14:8249–8258
Dweik M, Ferretti R (2022) Integrating anisotropic filtering, level set methods and convolutional neural networks for fully automatic segmentation of brain tumors in magnetic resonance imaging. Neurosci Info 2(3):100095
Han G, Shen S, Song H, Yang T, Zhang W (2018) A stratification-based data collection scheme in underwater acoustic sensor networks. IEEE Trans Veh Technol 67(11):10671–10682
Hemavathi S, Latha B (2023) HFLFO: Hybrid fuzzy levy flight optimization for improving QoS in wireless sensor network. Ad Hoc Netw 142:103110
Hosseini NejadTakhti A, Saffari A, Martín D, Khishe M, Mohammadi M (2022) Classification of marine mammals using the trained multilayer perceptron neural network with the whale algorithm developed with the fuzzy system. Comput Intel Neurosc 2022
Hou R, He L, Hu S, Luo J (2018) Energy-balanced unequal layering clustering in underwater acoustic sensor networks. IEEE Access 6:39685–39691
Jia H, Khishe M, Mohammadi M, Rashidi S (2022) Deep cepstrum-wavelet autoencoder: A novel intelligent sonar classifier. Expert Syst Appl 202:117295
Jin D, Yu Z, Jiao P, Pan S, He D, Wu J, Yu P, Zhang W (2021) A survey of community detection approaches: from statistical modeling to deep learning. IEEE Trans Knowl Data Eng
Kamalipour M, Agahi H, Khishe M, Mahmoodzadeh A (2023) Passive ship detection and classification using hybrid cepstrums and deep compound autoencoders. Neural Comput Appl 35(10):7833–7851
Khishe M (2023) Variable-length deep convolutional neural networks by Internet Protocol Addresses Whale Optimization Algorithm for random and complex image classification. Waves in Random and Complex Media 1–21
Khishe M, Mohammadi M, Mohammed AH (2022a) Complex active sonar targets recognition using variable length deep convolutional neural network evolved by biogeography-based optimizer. Waves in Random and Complex Media 1–25
Khishe M, Mohammadi M, Rashid TA, Mahmud H, Mirjalili S (2022b) Evolving deep neural network by customized moth-flame optimization algorithm for underwater targets recognition. In Handbook of Moth-Flame Optimization Algorithm. CRC Press. pp 53–76
Khishe M, Mohammadi M, RamezaniVarkani A (2023) Underwater backscatter recognition using deep fuzzy extreme convolutional neural network optimized via hunger games search. Neural Process Lett 55(4):4843–4870
Khishe M, Azar OP, Hashemzadeh E (2024) Variable-length CNNs evolved by digitized chimp optimization algorithm for deep learning applications. Multimed Tools Appl 83(1):2589–2607
Kosarirad H, Ghasempour Nejati M, Saffari A, Khishe M, Mohammadi M (2022) Feature selection and training multilayer perceptron neural networks using grasshopper optimization algorithm for design optimal classifier of big data sonar. J Sensors 2022
Kueppers S, Jaeschke T, Pohl N, Barowski J (2021) Versatile 126–182 GHz UWB D-band FMCW radar for industrial and scientific applications. IEEE Sensors Letters 6(1):1–4
Li Yu, Lin X, Liu J (2021) An improved gray wolf optimization algorithm to solve engineering problems. Sustainability 13(6):3208
Najibzadeh M, Mahmoodzadeh A, Khishe M (2023) Active Sonar Image Classification Using Deep Convolutional Neural Network Evolved by Robust Comprehensive Grey Wolf Optimizer. Neural Process Lett 55(7):8689–8712
Ossai CI, Wickramasinghe N (2022) GLCM and statistical features extraction technique with Extra-Tree Classifier in Macular Oedema risk diagnosis. Biomed Signal Process Control 73:103471
Rizzini DL, Kallasi F, Oleari F, Caselli S (2015) Investigation of vision-based underwater object detection with multiple datasets. Int J Adv Rob Syst 12(6):77
Saffari A, Khishe M, Zahiri SH (2022) Fuzzy-ChOA: an improved chimp optimization algorithm for marine mammal classification using artificial neural network. Analog Integr Circ Sig Process 111(3):403–417
Saffari A, Zahiri SH, Khishe M (2023) Automatic recognition of sonar targets using feature selection in micro-Doppler signature. Defence Technol 20:58–71
Saleem MH, Potgieter J, Arif KM (2022) Weed detection by faster RCNN model: An enhanced anchor box approach. Agronomy 12(7):1580
Sundarasekar R, Shakeel PM, Baskar S, Kadry S, Mastorakis G, Mavromoustakis CX, Samuel RDJ, Gn V (2019) Adaptive energy aware quality of service for reliable data transfer in under water acoustic sensor networks. IEEE Access 7:80093–80103
Tian Y, Khishe M, Karimi R, Hashemzadeh E, Pakdel Azar O (2023) Underwater image detection and recognition using radial basis function neural networks and chimp optimization algorithm. Circuits Syst Signal Process 42(7):3963–3982
Vankdothu R, Hameed MA (2022) Brain tumor MRI images identification and classification based on the recurrent convolutional neural network. Measurement: Sensors 24:100412
Vijay MM, Shalini Punithavathani D (2022) A memory-efficient adaptive optimal binary search tree architecture for IPV6 lookup address. In Mobile Computing and Sustainable Informatics. Springer, Singapore pp 749–764
Wang M, Chen Y, Sun X, Xiao F, Xu X (2020a) Node energy consumption balanced multi-hop transmission for underwater acoustic sensor networks based on clustering algorithm. IEEE Access 8:191231–191241
Wang Y, Tang C, Cai M, Yin J, Wang S, Cheng L, Wang R, Tan M (2020b) Real-time underwater onboard vision sensing system for robotic gripping. IEEE Trans Instrum Meas 70:1–11
Xiang Z, Zhu X, Jiang M, Quan L (2021) Multi-objective-layered optimization of a magnetic planetary gear for hybrid powertrain. IEEE J Emerg Sel Top Power Electron 10(1):934–944
Xue J, Shen B (2023) Dung beetle optimizer: A new meta-heuristic algorithm for global optimization. J Supercomput 79(7):7305–7336
Yan J, Yang X, Luo X, Chen C (2018) Energy-efficient data collection over AUV-assisted underwater acoustic sensor network. IEEE Syst J 12(4):3519–3530
Yang H, Liu P, Hu Y, Fu J (2021) Research on underwater object recognition based on YOLOv3. Microsyst Technol 27:1837–1844
Yeh CH, Lin CH, Kang LW, Huang CH, Lin MH, Chang CY, Wang CC (2021) Lightweight deep neural network for joint learning of underwater object detection and color conversion. IEEE Trans Neural Netw Learn Syst 33(11):6129–6143
Yu W, Chen Y, Wan L, Zhang X, Zhu P, Xu X (2020) An energy optimization clustering scheme for multi-hop underwater acoustic cooperative sensor networks. IEEE Access 8:89171–89184
Zhan C, Hu H, Liu Z, Wang Z, Mao S (2021) Multi-UAV-enabled mobile-edge computing for time-constrained IoT applications. IEEE Internet Things J 8(20):15553–15567
Zhang T, Zhu T, Li J, Han M, Zhou W, Philip SY (2020) Fairness in semi-supervised learning: Unlabeled data help to reduce discrimination. IEEE Trans Knowl Data Eng 34(4):1763–1774
Funding
Not applicable.
Author information
Authors and Affiliations
Contributions
The authors contributed to the design of the model, analysis of the data, and the computational framework. Material preparation and data collection were performed by Jeno and Edward. Muniraj verified the analytical methods and encouraged Jarin to investigate the possibility of ensemble learning and supervised the findings of this work. All authors discussed the results and contributed to the final manuscript. All the authors read and approved the final manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Ethics approval
Not applicable.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Conflict of interest
The authors declare no competing interests.
Additional information
Communicated by: Hassan Babaie
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Jasmine, J.J., Raja, S.E., Muniraj, R. et al. An improved dung beetle optimization with recurrent convolutional neural networks for efficient detection and classification of undersea water object images. Earth Sci Inform 17, 3651–3671 (2024). https://doi.org/10.1007/s12145-024-01358-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12145-024-01358-8