Devesh Kumar Srivastava; Chirag Goel; K. Kishore Kumar; Akhilesh Kumar Sharma; Babu R. Dawadi; Eshaan Saha

Enhancing Underwater Object Detection through CNN-based Image Enhancement and Classification

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Author(s)

Devesh Kumar Srivastava ¹ Chirag Goel ¹ K. Kishore Kumar ² Akhilesh Kumar Sharma ^3,* Babu R. Dawadi ⁴ Eshaan Saha ³

1. Department of Information Technology, Manipal University Jaipur, Jaipur, Rajasthan, India

2. Department of Electronics and Communication Engineering, The ICFAI University, Raipur, Chattisgarh, India

3. Department of Data Science and Engineering, Manipal University Jaipur, Jaipur, Rajasthan, India

4. Department of Electronics and Computer Engineering, Pulchowk Campus, Tribhuvan University, Kathmandu 19758, Nepal

* Corresponding author.

DOI: https://doi.org/10.5815/ijem.2026.02.06

Received: 23 Apr. 2025 / Revised: 22 Jun. 2025 / Accepted: 28 Sep. 2025 / Published: 8 Apr. 2026

Index Terms

Object detection, Convolutional Neural Networks (CNN), underwater image datasets, image enhancement, image illumination, feature learning, object classification, object detection, SDG Goal quality education, Life below water

Abstract

This research focuses on object detection using Convolutional Neural Networks (CNN) applied to underwater image datasets. Underwater images often suffer from issues such as low clarity and quality, which pose challenges for accurate object identification. To address this, the research employs image enhancement techniques, including image illumination methods, to improve image quality and facilitate object detection algorithms. Subsequently, the study developed algorithms capable of detecting objects and accurately predicting their categories. The primary objective is to achieve optimal accuracy and efficiency in underwater recognition. This research utilizes Machine Learning techniques through Tensor Flow and Image Processing to accomplish underwater object detection. Deep learning techniques, particularly feature learning, object classification, and detection, have gained significant attention and momentum. In this research we implemented different image enhancement techniques on dataset and evaluated their performance. While one metric, IQI (Image Quality Index), slightly favoured histogram equalization (HE), the other three metrics strongly favoured the enhanced version of HE known as Contrast Limited Adaptive Histogram Equalization (CLAHE).

Cite This Paper

Devesh Kumar Srivastava, Chirag Goel, K. Kishore Kumar, Akhilesh Kumar Sharma, Babu R. Dawadi, Eshaan Saha, "Enhancing Underwater Object Detection through CNN-based Image Enhancement and Classification", International Journal of Engineering and Manufacturing (IJEM), Vol.16, No.2, pp.91-110, 2026. DOI:10.5815/ijem.2026.02.06

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International Journal of Engineering and Manufacturing (IJEM)