Detection of Malaysian Traffic Signs via Modified YOLOv3 Algorithm
Wan-Noorshahida Mohd-Isa, Md-Shakif Abdullah, Mahmood Sarzil, Junaidi Abdullah, Aziah Ali, Noramiza Hashim
Abstract
Traffic signs images when captured in real environment are small in size compared to other objects. Thus, making it difficult to be accurately detected, more so identified. Of recent, the convolutional neural network (CNN) has been making tremendous progress in object detection due to its high accuracy and fast execution. YOLO (You Only Look Once) is an object detection method, which uses the CNN core module and suited for detection of traffic sign in real environment. In this paper, we implemented the YOLOv3 framework for identification of Malaysian traffic sign in real environment. With the inclusion of the spatial pyramid pooling (SPP) in our YOLOv3 framework, it enables the identification of far and small traffic signs in real environment due to pooling of multi-sizes grids of SPP. We presented results of this implementation on our data set of 2000 images of Malaysian traffic sign and the results at 0.5% confidence level are in the range of 90% with a mean average precision of 82.5%, which is promising.