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A New Real-Time Detection and Tracking Method in Videos for Small Target Traffic Signs
| Content Provider | MDPI |
|---|---|
| Author | Song, Shaojian Li, Yuanchao Huang, Qingbao Li, Gang |
| Copyright Year | 2021 |
| Description | It is a challenging task for self-driving vehicles in Real-World traffic scenarios to find a trade-off between the real-time performance and the high accuracy of the detection, recognition, and tracking in videos. This issue is addressed in this paper with an improved YOLOv3 (You Only Look Once) and a multi-object tracking algorithm (Deep-Sort). First, data augmentation is employed for small sample traffic signs to address the problem of an extremely unbalanced distribution of different samples in the dataset. Second, a new architecture of YOLOv3 is proposed to make it more suitable for detecting small targets. The detailed method is (1) removing the output feature map corresponding to the 32-times subsampling of the input image in the original YOLOv3 structure to reduce its computational costs and improve its real-time performances; (2) adding an output feature map of 4-times subsampling to improve its detection capability for the small traffic signs; (3) Deep-Sort is integrated into the detection method to improve the precision and robustness of multi-object detection, and the tracking ability in videos. Finally, our method demonstrated better detection capabilities, with respect to state-of-the-art approaches, which precision, recall and mAP is 91%, 90%, and 84.76% respectively. |
| Starting Page | 3061 |
| e-ISSN | 20763417 |
| DOI | 10.3390/app11073061 |
| Journal | Applied Sciences |
| Issue Number | 7 |
| Volume Number | 11 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-03-30 |
| Access Restriction | Open |
| Subject Keyword | Applied Sciences Transportation Science and Technology Object Detection Multi-object Tracking Improved Yolov3 Deep Learning Self-driving Vehicles |
| Content Type | Text |
| Resource Type | Article |
