Abstract
Vehicle detection plays an important role in safe driving assistance technology. Due to the high accuracy and good efficiency, the deformable part model is widely used in the field of vehicle detection. At present, the problem related to reduction of false positivity rate of partially obscured vehicles is very challenging in vehicle detection technology based on machine vision. In order to address the abovementioned issues, this paper proposes a deep vehicle detection algorithm based on the dual-vehicle deformable part model. The deep learning framework can be used for vehicle detection to solve the problem related to incomplete design and other issues. In this paper, the deep model is used for vehicle detection that consists of feature extraction, deformation processing, occlusion processing, and classifier training using the back propagation (BP) algorithm to enhance the potential synergistic interaction between various parts and to get more comprehensive vehicle characteristics. The experimental results have shown that proposed algorithm is superior to the existing detection algorithms in detection of partially shielded vehicles, and it ensures high detection efficiency while satisfying the real-time requirements of safe driving assistance technology.
Highlights
Nowadays, vehicle traffic accidents cause about 12 million casualties and 1–3% of the total global GDP loss of social property
In order to further improve the accuracy of vehicle detection, this paper proposes a vehicle detection algorithm based on the dualvehicle depth model
In order to verify the vehicle detection algorithm based on the dual-vehicle depth model, the algorithm was validated on the KITTI dataset
Summary
Vehicle traffic accidents cause about 12 million casualties and 1–3% of the total global GDP loss of social property. The major causes of road accidents are related with the subjective factors of drivers. It is imperative to improve road safety and help drivers to anticipate and avoid traffic accidents. The vehicle vision detection based on machine vision is a hotspot in the field of computer vision and safe driving aids. Many scholars have applied pattern recognition, image processing, and machine learning to the field of vehicle detection and have achieved good results that have played an important role in basic research and engineering application [1,2,3,4,5]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
