Name: JEAN PABLO VIEIRA DE MELLO
Publication date: 27/07/2021
Advisor:
Name |
Role |
|---|---|
| THIAGO OLIVEIRA DOS SANTOS | Advisor * |
Examining board:
| Name |
Role |
|---|---|
| THIAGO OLIVEIRA DOS SANTOS | Advisor * |
| ALBERTO FERREIRA DE SOUZA | Internal Examiner * |
Summary: The use of deep neural networks as a solution to problems related to autonomous driving
has been increasingly considered by the researchers. With this tooling, common traffic
elements, such as pedestrians, traffic signs and traffic lights can be detected effectively,
by simply providing as input data a representative amount of images that describe a real
traffic context. In particular, the detection of traffic lights and the correct classification of
their state are essential in preventing accidents. However, collecting and annotating such
set of traffic light data can be a highly costly task, both in time and effort. To overcome
this problem, it is proposed assembling an expressive dataset that overlaps traffic contexts
generated synthetically, through simple computer graphics, on arbitrary images containing
natural scenes not related to traffic. This dispenses the need for collection of real-world
data, automates the annotation of traffic lights arranged in the generated scene, and also
makes it possible to balance the occurrences of the yellow state, which would be difficult
to capture, with those of the other states. Experiments revealed that using the method
yields results comparable to those obtained using real-world data, with average mAP and
F1-score about 4 percent points higher.
Keywords: traffic light. synthetic context. deep detection. Computer Graphics. natural
images.
