MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

The researchers face a difficult problem in detecting and identifying underwater fish species. Marine researchers and ecologists must evaluate the comparative profusion of fish species in their environments on a regular basis and track population trends. Researchers have presented a number of underwater computer vision, machine learning-based automatic systems for fish detection and classification. However, due of the changing undersea environment, it is extremely challenging to find the ideal system for detecting and classifying fish. Because light has such a strong influence in the aqueous medium, conducting research in this environment is difficult. The MobileNet model is utilised to detect and recognise the fish breed in the proposed work. The dataset is preprocessed before the model is implemented in order to obtain appropriate performance metrics. The work is based on the Kaggle dataset, which has nine different fish breeds in total. With a 99.74 percent accuracy, the model can detect and recognise nine different breeds. In comparison to other state of art methods, the model exhibits promising results. © 2022 IEEE.

School of Electronics & Communication Engineering

Engineering and Technology

2022 International Conference on Intelligent Controller and Computing for Smart Power, ICICCSP 2022

Underwater Fish Detection and Classification using Deep Learning

Remotely operated vehicles (ROVs) are underwaterrobots that are commanded from the surface by a person. This robot is made up of embedded systems and mechanical systems, and it is connected to the outside world via a series ofload-bearing umbilical cables that hold power, data, and communication cables. For the ROV, data transfer as well ascontrol signals are required. The navigation team was made up of working on the ROV's navigation system. It has a video camera and a ballast system for balance, movement propulsion system pumps, and IMU to locate the position of a vehicle microcontrollers to compute, process, and offer the navigation system, sensor for feedback data back to the lighting and components controlling and coordinating all of this is a challenge. A manipulator, subsea equipment, and devices to assess clarity, temperature, and depth are all included in the proposed task. The purpose of the proposed effort is to create such a ROV to execute a given mission involving several persons. In addition, a novel form of ROV attitude control is introduced, which makes use of floats to modify the ROV's centre of buoyancy. This is an interdisciplinary endeavour in which the members are from many fields. Together with the mechanical department, the electronics navigation team worked on this. © 2022 IEEE.

World University root node

Proceedings - 2022 International Conference on Recent Trends in Microelectronics, Automation, Computing and Communications Systems, ICMACC 2022

Navigation of Underwater Remotely Operated Vehicle

The aim of image captioning research is to caption and annotate an image with a sentence that explains the image. It's not tough for humans but it is for machines, to make sense out of what is actually there but not seen. Facebook and Google, for example, use image recognition to monitor where you are, what you do, and other activities. In Proposed work, natural language processing and Deep Learning applied through Object Detection and Text Generation is used to generate description of an image automatically. This architecture includes a dense attention model with a CNN Encoder and RNN Decoder, an average BLEU score of 51.77 was observed. © 2021 IEEE.

2021 International Conference on Computational Performance Evaluation, ComPE 2021

Image Captioning using Deep Learning

The development in ocean exploration and observation make the demand for moving object detection in underwater increasingly urgent. The moving object detection in underwater with dynamic or moving background is the challenging and difficult task for the researchers. In dynamic environment, foreground object as well as background of the scene both are in motion condition, thus it is very difficult to detect foreground moving object in underwater medium. In proposed work, background is categorized into three types i.e. static, moderate dynamic and high dynamic backgrounds. The GMG algorithm invented by Andrew B. Godbehere, Akihiro Matsukawa, Ken Goldberg is implemented to detect moving object and compare the results for the three background categories. The sensitivity and accuracy parameter values are considered for the comparison of the results. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Advances in Intelligent Systems and Computing

Underwater Moving Object Detection Using GMG

Detecting a moving object is very crucial in surveillance application. Many algorithms have been developed for object detection, recognition and analysis. The algorithms involve the trajectory of detected object by locating position in each captured image. In the proposed work a system is implemented to detect foreground object in any environmental condition and the model is implemented on FPGA board. Real time foreground object detection is a very complex task. Proposed system detects object by background subtraction algorithm based on dynamic thresholding. Background subtraction algorithm detects moving object by subtracting current frame from reference background frame. The system is simulated using MATLAB and implemented on FPGA board. © 2019 IEEE.

2019 IEEE Pune Section International Conference, PuneCon 2019

Detection of Moving Object Based on FPGA

The detection of moving object is fundamental to automated video surveillance, object analysis and recognition. It involves the trajectory of an object over time by locating its position in every frame of the video. In this paper we implement a moving object detection method on FPGA. Detection of moving object is a challenging task. In our implementation of the design we used the background subtraction based method. Background subtraction is a method which detects moving objects from reference background image. Experiment is carried out in MATLAB and on FPGA board. © 2014 IEEE.