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A Multibranch Object Detection Method for Traffic Scenes.
- Source :
-
Computational Intelligence & Neuroscience . 11/11/2019, p1-16. 16p. - Publication Year :
- 2019
-
Abstract
- The performance of convolutional neural network- (CNN-) based object detection has achieved incredible success. Howbeit, existing CNN-based algorithms suffer from a problem that small-scale objects are difficult to detect because it may have lost its response when the feature map has reached a certain depth, and it is common that the scale of objects (such as cars, buses, and pedestrians) contained in traffic images and videos varies greatly. In this paper, we present a 32-layer multibranch convolutional neural network named MBNet for fast detecting objects in traffic scenes. Our model utilizes three detection branches, in which feature maps with a size of 16 × 16, 32 × 32, and 64 × 64 are used, respectively, to optimize the detection for large-, medium-, and small-scale objects. By means of a multitask loss function, our model can be trained end-to-end. The experimental results show that our model achieves state-of-the-art performance in terms of precision and recall rate, and the detection speed (up to 33 fps) is fast, which can meet the real-time requirements of industry. [ABSTRACT FROM AUTHOR]
- Subjects :
- *TRAFFIC monitoring
*PEDESTRIANS
*ARTIFICIAL neural networks
*BUSES
Subjects
Details
- Language :
- English
- ISSN :
- 16875265
- Database :
- Academic Search Index
- Journal :
- Computational Intelligence & Neuroscience
- Publication Type :
- Academic Journal
- Accession number :
- 139615192
- Full Text :
- https://doi.org/10.1155/2019/3679203