Object detection algorithm based on improved Yolov3-tiny network in traffic scenes
- Resource Type
- Conference
- Authors
- Wang, Zhenghao; Li, Linhui; Li, Lei; Pi, Jiahao; Li, Shuoxian; Zhou, Yafu
- Source
- 2020 4th CAA International Conference on Vehicular Control and Intelligence (CVCI) Vehicular Control and Intelligence (CVCI), 2020 4th CAA International Conference on. :514-518 Dec, 2020
- Subject
- Power, Energy and Industry Applications
Transportation
Deep learning
Intelligent vehicles
Fuses
Object detection
Predictive models
Real-time systems
Stereo vision
object detection
Yolo-tiny
environment perception
stereo vision
- Language
The object detection based on deep learning is an important application in the field of vehicle environment perception, which has been a hot topic in recent years. We propose a novel improved Yolov3-tiny to implement more accurate object detection for the objects in traffic scenes. We employ K-means algorithm to cluster the common objects in traffic scenes to obtain a suitable size and numbers of anchor box. In addition, we modify modifying detection scale and the backbone network structure of Yolov3-tiny, improving the detection accuracy for small object. The stereo vision is also introduced to improve the accuracy of boundary location. Experiments results demonstrate that the improved yolo-tiny has higher accuracy than the original algorithm and it also meet the requirement of real-time performance.