General object-detection methods based on deep learning have received considerable attention in the field of computer vision. However, when they are applied to vehicle detection (VD) in a straightforward manner to realize an intelligent vehicle (IV), a graphics processing unit (GPU) is required for their real-time implementation. The use of GPUs is unacceptable in commercial VD systems. A novel on-road VD method comprising the use of a multi-stage convolutional neural network (MSCNN) is proposed to solve this problem. In the MSCNN, the properties of the vehicles are exploited, and an efficient region proposal specialized for vehicles is developed. The proposed MSCNN comprises four stages: vehicle lower-boundary detection, vehicle upper-boundary detection, region proposal network (RPN), and vehicle classification. Effective anchor boxes are generated in the lower- and upper-boundary-detection stages with appropriate sizes for vehicles of all scales. The bounding box of the vehicle within the anchor box is determined in the RPN stage. In the last stage, the predicted bounding boxes are classified as vehicles or non-vehicles. Finally, the proposed method is applied to the KITTI, CrowdAI, and AUTTI datasets, and its advantages are demonstrated by comparing its performance with those presented in previous studies. The proposed MSCNN realizes an average precision (72.1%) on the KITTI dataset while running on a central processing unit (CPU).
|Number of pages||15|
|Publication status||Published - 2021|
Bibliographical noteFunding Information:
This work was supported by the Institute of Information and Communications Technology Planning and Evaluation (IITP) grant through the Korea Government [Ministry of Science and ICT (MSIT)], Development of Driving Environment Data Transformation and Data Verification Technology for the Mutual Utilization of Self-Driving Learning Data for Different Vehicles under Grant 2021-0-00800.
© 2013 IEEE.
All Science Journal Classification (ASJC) codes
- Computer Science(all)
- Materials Science(all)
- Electrical and Electronic Engineering