Abstract
An exponential growth in advanced technologies has resulted in the exploration of Ocean spaces. It has paved the way for new opportunities that can address questions relevant to diversity, uniqueness, and difficulty of marine life. Underwater Wireless Sensor Networks (UWSNs) are widely used to leverage such opportunities while these networks include a set of vehicles and sensors to monitor the environmental conditions. In this scenario, it is fascinating to design an automated fish detection technique with the help of underwater videos and computer vision techniques so as to estimate and monitor fish biomass in water bodies. Several models have been developed earlier for fish detection. However, they lack robustness to accommodate considerable differences in scenes owing to poor luminosity, fish orientation, structure of seabed, aquatic plant movement in the background and distinctive shapes and texture of fishes from different genus. With this motivation, the current research article introduces an Intelligent Deep Learning based Automated Fish Detection model for UWSN, named IDLAFD-UWSN model. The presented IDLAFD-UWSN model aims at automatic detection of fishes from underwater videos, particularly in blurred and crowded environments. IDLAFD-UWSN model makes use of Mask Region Convolutional Neural Network (Mask RCNN) with Capsule Network as a baseline model for fish detection. Besides, in order to train Mask RCNN, background subtraction process using Gaussian Mixture Model (GMM) model is applied. This model makes use of motion details of fishes in video which consequently integrates the outcome with actual image for the generation of fish-dependent candidate regions. Finally, Wavelet Kernel Extreme Learning Machine (WKELM) model is utilized as a classifier model. The performance of the proposed IDLAFD-UWSN model was tested against benchmark underwater video dataset and the experimental results achieved by IDLAFD-UWSN model were promising in comparison with other state-of-the-art methods under different aspects with the maximum accuracy of 98% and 97% on the applied blurred and crowded datasets respectively.
Highlights
Water covers 75% of earth’s surface in the form of different water bodies such as canals, oceans, rivers, and seas
Though TKDE and FLDA-TM models showcased competitive results with its accuracy values being 85.90% and 89%, the presented IDLAFD-Underwater Wireless Sensor Networks (UWSNs) model achieved the maximum accuracy of 97%
The presented IDLAFD-UWSN model aims at automatic detection of fishes from underwater videos, in blurred and crowded environments
Summary
Water covers 75% of earth’s surface in the form of different water bodies such as canals, oceans, rivers, and seas. Tracking is a complex procedure that aims at determining the condition (such as acceleration, position, and velocity) of one or more quickly-moving targets and nearby the actual condition, by utilizing the presented measurement gathered from several sensors. This information is crucial in war atmosphere for two main causes. A substantial number of studies has examined the challenges faced in target tracking in terrestrial atmosphere In these studies, the system depends upon different kinds of sensors which could be applied for detecting and tracking the target.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
