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Impact of Machine Learning Algorithms in Under Water Image Enhancement and Object Detection
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Author(s): Naveena Treesa (Amal Jyothi College of Engineering, India), S. N. Kumar (Amal Jyothi College of Engineering, India)and Jomin Joy (Amal Jyothi College of Engineering, India)
Copyright: 2027
Pages: 14
Source title:
Encyclopedia of Modern Artificial Intelligence
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Founding Editor-in-Chief, Information Resources Management Journal (IRMJ), USA)
DOI: 10.4018/408155
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Abstract
Underwater image analysis is often riddled with its own unique challenges, among them poor lighting, water turbidity, and color distortion. The presence of all these factors makes it quite challenging for traditional image processing techniques to enhance the underwater image to facilitate object identification. These recent works on improving underwater images and object detection have become a hot topic in machine learning algorithms and provide promising solutions in this regard. This article provides an overview of machine learning algorithms to enhance underwater images and detect underwater objects. It first elaborates on the inherent difficulties related to underwater imagery, underpinning the state of the art of traditional image processing methods. The following discussion develops how many ML technologies, among CNNs, RNNs, GANs, have been used for responding to these challenges. In this article, the details of how machine learning algorithms use enhancement of underwater images by reducing the noise and removing color distortion for better visibility have been discussed.
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