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Image recognition under water - enhance fisheries management and/or water quality assessments




2: Mechanical Engineering

Faculty Supervisor:

Brian Anthony

Faculty email:


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Project Description

Marine fisheries populations have a large impact on the U.S. economy – from commercial fishing to coastal communities. Overfishing, barriers to migration, and other forms of human activity may impact spawning patterns of these species. Therefore, it is necessary to monitor these populations to maintain sustainable resources, healthy oceans, and marine life. Federal and state agencies deploy camera equipment to monitor fisheries populations. Employees then manually count the number of specimens in the gathered videos and images. Not only is this an inefficient use of resources and employee time, but it can also lead to inaccurate results due to human error. A closely related problem, of interest to some of the industry mentors, is optical bacteria recognition, tracking, and counting. Through the application of deep learning-based image recognition, identification of target species in video and image data can be automated. Current state-of-the-art image recognition relies on Convolutional Neural Networks (CNNs) to achieve learning and recognition. CNNs loosely represent biological neural networks: each neuron, or layer, accomplishes a specific task, such as edge detection. Impact: This project is of interest to fisheries and other animal monitoring applications and water monitoring applications. Such algorithms can be adopted to enhance the capabilities of Fisheries Management in monitoring fisheries populations or adopted to aid in water quality measurement.


1. This project will utilize machine learning and data analytics, image visualization, and process modelling skills. Background/interest in these skills are helpful and students can expect to enhance these skills through the project. 2. This project is offered as part of the MechE Alliance industry connected ELO cohorts. Applicants will be expected to participate in the cohort program to be eligible for the position. More information can be found in this Google Doc: https://drive.google.com/file/d/1YbGwDXGAvPDSN6DEDGBw-VDJWS7jnp65/view?usp=sharing This is a remote UROP opportunity.