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

• Main Authors: Junping Hu, Shitu Abubakar, Shengjun Liu, Xiaobiao Dai, Gen Yang, Hao Sha
• Format: Article
• Language: English
• Published: Hindawi Limited 2019-01-01
• Series: Journal of Sensors
• Online Access: http://dx.doi.org/10.1155/2019/7174602

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.