Open AccessMulti-scale CNN Approach for Accurate Detection of Underwater Static Fish Image
Author(s) -
Kottilingam Kottursamy
Publication year - 2021
Publication title -
journal of artificial intelligence and copsule networks
Language(s) - English
Resource type - Journals
ISSN - 2582-2012
DOI - 10.36548/jaicn.2021.3.006
Subject(s) - underwater , computer science , artificial intelligence , segmentation , focus (optics) , robot , scale (ratio) , identification (biology) , process (computing) , fish <actinopterygii> , computer vision , object detection , image segmentation , object (grammar) , scale space , graph , image (mathematics) , image processing , geography , fishery , ecology , cartography , archaeology , theoretical computer science , physics , optics , biology , operating system
Recently, the identification and naming of fish species in underwater imagery processing has been in high demand. This is an essential activity for everyone, from biologists to scientists to fisherman. Humans' interests have recently expanded from the earth to the sky and the sea. Robots could be utilized to send mankind to explore the ocean and outer space, as well as for some dangerous professions that human beings are unlikely to perform. Humans have recently shifted their focus from land-based exploration to celestial exploration and the sea. Robots are used for the activities that pose a risk to mankind, like exploration of the seas and outer space. This research article provides a solution to underwater image detection techniques by using an appended transmission map, refinement method and deep learning approach. The features are deeply extracted by multi-scale CNN for attaining higher accuracy in detecting fish features from the input images with the help of segmentation process. Object recognition errors are minimized and it has been compared with other traditional processes. The overall performance metrics graph has been plotted for the proposed algorithm in the results and discussion section.