Deep learning based computer vision for aerial-view street object detection and classification
Correu electrònic de l'autorrcolljosifovgmail.com
Tipus de documentTreball Final de Grau
Data2022-07-14
Condicions d'accésAccés obert
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Abstract
This project consists of the training of a convolutional neural network based on the YOLO approach for the systematic detection and classification of objects in a street as seen from a drone flying over them, to then be used for an algorithm to predict these objects’ paths and consider possible accidents between them. In the first part, a graphic user interface for dataset creation and validation has been created, with the goal of allowing the creation and validation of other datasets in order to have a custom-made dataset for the goal. The dataset based on is the VisDrone dataset, which consists only of drone-view imagery of streets. After this, using the Darknet framework, several YOLO and tiny-YOLO models have been trained through Google Colab cloud computing services, and then all these have been tested in front of other data and compared to other pre-existing models in the field. Finally, the outputs of the YOLO network is then used in an algorithm to predict possible accidents on streets. This is based on logic-based algorithms for object tracking, linear regression for path prediction, and solver of linear system of equations for the detection of possible collisions, followed by filtering of these. Overall, a YOLO model with a mean average precision of 75% on the development set and 62% on the test set has been trained, and preliminary positive results have come from the accident prediction algorithm
MatèriesComputer vision, Deep learning (Machine learning), Machine learning, Neural computers, Artificial intelligence, Data sets, Visió per ordinador, Aprenentatge profund, Aprenentatge automàtic, Ordinadors neuronals, Intel·ligència artificial, Conjunts de dades
TitulacióGRAU EN ENGINYERIA ELECTRÒNICA INDUSTRIAL I AUTOMÀTICA (Pla 2009)
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Report_Bachelor_Thesis_Richard_Coll.pdf | 97,39Mb | Visualitza/Obre | ||
Budget_Bachelor_Thesis_Richard_Coll.pdf | 240,7Kb | Visualitza/Obre |