Classification of traffic signs based on object recognition
Traffic lights recognition and classification play an important role in the realization of autonomous vehicles. This automated process uses video (frames) to recognize and classify traffic lights along the vehicle’s path in real time. In this thesis, we adapt a proven deep learning model to re...
Αποθηκεύτηκε σε:
| Κύριος συγγραφέας: | |
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| Άλλοι συγγραφείς: | |
| Γλώσσα: | English |
| Δημοσίευση: |
2022
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| Θέματα: | |
| Διαθέσιμο Online: | http://hdl.handle.net/11610/23103 |
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| Περίληψη: | Traffic lights recognition and classification play an important role in the realization of
autonomous vehicles. This automated process uses video (frames) to recognize and
classify traffic lights along the vehicle’s path in real time. In this thesis, we adapt a
proven deep learning model to recognize three classes (states) of traffic lights: green,
red and yellow. The model is YOLOv3, it includes Darknet-53 and combines object
detection and classification. The deep learning algorithms are implemented in Google
Colab (a cloud platform developed by Google). The resulting Convolutional Neural
Network (CNN) is trained using publicly available data sets that we modify to enhance
the available training data.
Firstly, the training and validation datasets are generated. Secondly, ground truth
bounding boxes -which define the class and the object in the images- are created and
uploaded to the Colab environment that runs the algorithm for object detection. The
algorithm preprocesses the images, creates bounding boxes that contain the object
and adjusts the weights of some model layers. To obtain the most appropriate
weights, we perform various training/validation experiments using combinations of
available datasets. The experiments indicate that the addition of very clear photos,
which contain only traffic lights in the training datasets, and in general photos in which
traffic lights are part of a general environment, significantly help the training process.
We use the best performing weights to conduct a large case study that uses as input
video footage taped from the streets of Thessaloniki, which contains numerous traffic
lights in all three states. We divide the predictions into 3 categories: True, False and
No predictions. Initially the study indicated a relatively low performance of the model
caused by a high percentage of No predictions. To address this issue, we used more
than one photographs of each traffic light-state combination and combined the
related predictions. As a result, the percent of No predictions was reduced
significantly, and the combined process yielded better results. |
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