Name
Abstract | ||
|---|---|---|
Vehicle detection is one of the important applications of object detection in intelligent transportation systems. It aims to extract specific vehicle-type information from pictures or videos containing vehicles. To solve the problems of existing vehicle detection, such as the lack of vehicle-type recognition, low detection accuracy, and slow speed, a new vehicle detection model YOLOv2 Vehicle based on YOLOv2 is proposed in this paper. The k-means++ clustering algorithm was used to cluster the vehicle bounding boxes on the training dataset, and six anchor boxes with different sizes were selected. Considering that the different scales of the vehicles may influence the vehicle detection model, normalization was applied to improve the loss calculation method for length and width of bounding boxes. To improve the feature extraction ability of the network, the multi-layer feature fusion strategy was adopted, and the repeated convolution layers in high layers were removed. The experimental results on the Beijing Institute of Technology (BIT)-Vehicle validation dataset demonstrated that the mean Average Precision (mAP) could reach 94.78%. The proposed model also showed excellent generalization ability on the CompCars test dataset, where the "vehicle face" is quite different from the training dataset. With the comparison experiments, it was proven that the proposed method is effective for vehicle detection. In addition, with network visualization, the proposed model showed excellent feature extraction ability. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3390/s18124272 | SENSORS |
Keywords | Field | DocType |
vehicle detection,object detection,YOLOv2,convolutional neural network | Automotive engineering,Electronic engineering,Vehicle detection,Engineering | Journal |
Volume | Issue | ISSN |
18 | 12.0 | 1424-8220 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Sang | 1 | 40 | 12.62 |
| Zhongyuan Wu | 2 | 1 | 2.05 |
| Pei Guo | 3 | 0 | 0.34 |
| Haibo Hu | 4 | 1068 | 66.30 |
| Hong Xiang | 5 | 26 | 5.56 |
| Qian Zhang | 6 | 290 | 43.11 |
| Bin Cai | 7 | 0 | 2.37 |