Evaluation of algorithms for traffic sign detection - López Montiel, Miguel (My University, 2019)
Traﬃc sign detection is a crucial task in autonomous driving systems. Due to its importance, several techniques have been used to solve this problem. In this work, the three more common approaches are evaluated. The ﬁrst approach uses a model of the traﬃc sign which is based in color and shape. The second one enhances the image model of the ﬁrst approach using K-means for color clustering. The last approach uses convolutional neural networks designed for image detection. The LISA Traﬃc Sign Dataset was used which it was divided into three superclasses: prohibition, mandatory, and warning signs. The evaluation was done using objective metrics used in the state-of-the-art.
Keywords: Detection; Traﬃc sign; Machine learning; Computer vision; Deep learning; Autonomous vehicles; Campus Tijuana