Cover
Vol. 22 No. 1 (2026)

Published: June 15, 2026

Pages: 363-373

Original Article

Enhancing Underwater Search and Rescue Operations: A CNN Approach for Human, Fish, and Plant Classification

Ali Fathel Rasheed 1 Rabee M. Hagem 2 Abdul Sattar Mohammed Khidhir 3
1 Mathematics Department, College of Basic Education, University of Telafer, Mosul, Iraq
2 Computer Engineering Department, College of Engineering, University of Mosul Mosul, Iraq
3 Electronics Technology Department, Mosul Technical Institute, Northern Technical University, Mosul, Iraq
History
  • Received: March 10, 2024
  • Revised: July 13, 2024
  • Accepted: July 19, 2024
  • Available Online: June 15, 2026
DOI

https://doi.org/10.37917/ijeee.22.1.32

Abstract

In recent times, artificial intelligence has become an essential part of our lives, particularly in tasks involving object recognition. This paper explores the use of convolutional neural networks (CNNs) for enhancing underwater search and rescue operations by classifying images of humans, fish, and plants. Leveraging the OpenCV library for preprocessing and the Keras library with a TensorFlow backend for recognition, this study utilizes a dataset captured through field experiments. The methodology involved preprocessing the images for segmentation, followed by training a CNN model to classify these images with high accuracy. The CNN model demonstrated a remarkable classification accuracy of 99.6 %, significantly outperforming other modern machine-learning methods. This work suggests that CNNs can greatly improve the speed and effectiveness of underwater search and rescue operations by accurately identifying and locating submerged persons, which is critical for timely rescue missions.

Keywords

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