Finding every car: a traffic surveillance multi-scale vehicle object detection method
| Autor: | Ling-Qun Zuo, Rui-Sheng Jia, Qi-Chao Mao, Hong-Mei Sun |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
business.industry
Computer science Scale (descriptive set theory) 02 engineering and technology Object detection Artificial Intelligence Feature (computer vision) Pyramid 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer vision Artificial intelligence Pyramid (image processing) Layer (object-oriented design) business |
| Zdroj: | Applied Intelligence. 50:3125-3136 |
| ISSN: | 1573-7497 0924-669X |
| DOI: | 10.1007/s10489-020-01704-5 |
| Popis: | According to the problem that the multi-scale vehicle objects in traffic surveillance video are difficult to detect and the overlapping objects are prone to missed detection, an improved vehicle object detection method based on YOLOv3 was proposed. In order to extract feature more efficiently, we first use the inverted residuals technique to improve the convolutional layer of YOLOv3. To solve the multi-scale vehicle object detection problem, three spatial pyramid pooling(SPP) modules are added before each YOLO layer to obtain multi-scale information. In order to cope with the overlapping of vehicles in traffic videos, soft non maximum suppression (Soft-NMS) is used to replace non maximum suppression (NMS), thereby reducing the missing of predicted boxes due to vehicle overlaps. Our experiment results in the Car dataset and the KITTI dataset confirm that the proposed method achieves good detection results for vehicle objects of various scales in various scenes. Our method can meet the needs of practical applications better. |
| Databáze: | OpenAIRE |
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