Near-Infrared Road-Marking Detection Based on a Modified Faster Regional Convolutional Neural Network

Abstract EN

Pedestrians, motorist, and cyclist remain the victims of poor vision and negligence of human drivers, especially in the night.

Millions of people die or sustain physical injury yearly as a result of traffic accidents.

Detection and recognition of road markings play a vital role in many applications such as traffic surveillance and autonomous driving.

In this study, we have trained a nighttime road-marking detection model using NIR camera images.

We have modified the VGG-16 base network of the state-of-the-art faster R-CNN algorithm by using a multilayer feature fusion technique.

We have demonstrated another promising feature fusion technique of concatenating all the convolutional layers within a stage to extract image features.

The modification boosts the overall detection performance of the model by utilizing the advantages of the shallow layers and the deep layers of the VGG-16 network.

The training samples were augmented using random rotation and translation to enhance the heterogeneity of the detection algorithm.

We have achieved a mean average precision (mAP) of 89.48% and 92.83% for the baseline faster R-CNN and our modified method, respectively.