International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Intelligent Underwater Object Detection Using CNN + SVM

Author(s) Dr. Shweta Suryawanshi, Mr. Rahul Sharma, Ms. Kalyani Ekhande, Mr. Vijay Biradar
Country India
Abstract Underwater object detection finds its paramount importance in various applications like marine exploration, ocean monitoring, and underwater surveillance. However, underwater images usually pose challenges such as low visibility, light scattering, color attenuation, and noise that seriously deteriorate the detection performance. An intelligent underwater object detection approach is proposed by using a hybrid Convolutional Neural Network-Support Vector Machine model to handle these issues. In this proposed method, the CNN is used for automatic deep feature extraction from underwater images while a support vector machine classifies those features with robustness. The hybrid CNN-SVM framework effectively unifies the feature learning capability of the deep learning methods and the strong generalization ability of the machine learning methods. The proposed approach has been evaluated on the available, publicly shared underwater image datasets containing fish, corals, underwater vegetation, rocks, and man-made objects. Experimental results show that the CNN-SVM model can achieve higher accuracy, precision, recall, and F1
score as compared to stand-alone CNN and transfer learning models like VGG19. This demonstrates that the proposed system is reliable, robust, and well-suited for underwater object detection under challenging environmental conditions.
Keywords Underwater Image Processing, Object Detection, Convolutional Neural Network, Support Vector Machine, Hybrid Learning Model, Marine Object Recognition
Field Engineering
Published In Volume 8, Issue 1, January-February 2026
Published On 2026-02-04
DOI https://doi.org/10.36948/ijfmr.2026.v08i01.67671
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