October - July 2022
The project involves the implementation and training of different neural models (NL-CNN, MicronNet, Mobile-Net) with different databases (GTSRB, BTSD) and tracking for each the optimization with obtaining those better functional performance (recognition accuracy) under conditions of response times and occupancy a as little memory as possible.
After finding an optimal classification model, a detection method was sought traffic signs in the images. The YOLO algorithm was used for detection and was trained with different databases.
The optimal neural models were saved in Tensorflow Lite format and methods for transferring them to an Android application were investigated. Android application must be capable of realizing the function of recognizing traffic signs from new images entered by the user.
In order to input images based on which to perform the recognition function, two pages were made in application framework: a page where recognition is done in real time by processing images taken from the user's camera and a page where the user can choose images from the device's storage.
The neural networks for traffic sign classification that were studied in the paper are NL-CNN, MicronNet and MobileNet. These were optimized for the GTSRB and BTSD datasets.
For traffic sign detection, the compact algorithms from the last two versions of YOLO were trained currently available: YOLOv4 Tiny, YOLOv5 s and YOLOv5 n.
To create the Android application, several implementation options were studied. The solution proposed in the project it is based on very good optimization of neural models and data processing directly on the device without being internet connection required. The advantage of this implementation is the exclusive use of resources devices, not requiring a constant connection to a server, and the possible disadvantage is given by the limitations computational devices, if the neural models are not optimized enough.
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Bucharest • Romania