Name
Title | ||
|---|---|---|
Near-Infrared Road-Marking Detection Based on a Modified Faster Regional Convolutional Neural Network |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1155/2019/7174602 | JOURNAL OF SENSORS |
DocType | Volume | ISSN |
Journal | 2019 | 1687-725X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junping Hu | 1 | 0 | 0.34 |
| Shitu Abubakar | 2 | 0 | 0.34 |
| Shengjun Liu | 3 | 116 | 13.79 |
| Xiaobiao Dai | 4 | 0 | 0.34 |
| Gen Yang | 5 | 0 | 0.34 |
| Hao Sha | 6 | 0 | 0.34 |