{"id":17994318,"url":"https://github.com/wesleylp/dissertation","last_synced_at":"2026-03-19T02:46:04.700Z","repository":{"id":145634792,"uuid":"172593610","full_name":"wesleylp/dissertation","owner":"wesleylp","description":"Dissertation for the degree of M. 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Sc.)\nin Electrical Engineering.\n\n\n\n## Abstract\n\nEvery year, thousands of people are infected with diseases such as dengue, chikungunya, zika, and yellow fever.\nThese diseases are transmitted by the Aedes aegypti, which usually reproduces in containers with accumulated clean water, such\nas tires, bottles, water tanks, etc.\nThe use of intelligent tools can be employed to assist health agents in a search for these objects, providing more efficiency and coverage in this process.\nThis work addresses the problem of automatic detection of\nsuch mosquito breeding grounds using computer vision and machine learning techniques. In this context, a new aerial videos dataset is devised including such objects in\ndifferent scenarios: distinct backgrounds, altitudes, object displacement, and so on.\nThe videos are rectified in order to compensate for camera distortions and manually\nannotated, frame-by-frame, enabling the development of an automatic detector for\nthe target objects.\nA Faster Region-based Convolutional Neural Network detector is trained, using a\nsmall dataset, and is capable of finding potential mosquito foci. This model achieves\n49.31 points of average precision, which is promising, indicating that new and better\nmodels can be trained for this task.\n\n# Compiling\n1. Open terminal and type:\n```\n# clone the repository\n$ git clone https://github.com/wesleylp/dissertation.git\n$ cd dissertation\n```\n\n2.  Download the [zip](https://drive.google.com/open?id=1pf2yXcRG-GbvqYI_-WUFJYBXKuRvqYrB) file containing the images and extract the folder to `dissertation`.\n\n## Text\nIn the terminal type:\n```\n$ make\n```\n\n## Presentation\nIn the terminal type:\n```\n$ cd presentation\n$ make\n```\n# Cite\n```\n@MastersThesis{passos2019,\n  Title                    = {Automatic Aedes aegypti breeding grounds detection using computer vision techniques},\n  Author                   = {Passos, Wesley Lobato},\n  School                   = {COPPE/UFRJ},\n  Year                     = {2019},\n  Address                  = {Rio de Janeiro, RJ, Brazil},\n  Type                     = {{M.Sc.} Dissertation}\n}\n```","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwesleylp%2Fdissertation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fwesleylp%2Fdissertation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fwesleylp%2Fdissertation/lists"}