Abstract
Real-time vehicle detection is a challenging and vital task in intelligent transportation systems. The key requirements for a vehicle detection model are speed and accuracy. However, existing real-time vehicle detection models often sacrifice one of these qualities in favor of the other. This trade-off makes them unfit for real-time deployment, where both speed and accuracy are equally important. Additionally, occlusion, which refers to the obstruction or partial covering of vehicles, further complicates detection and affects the system’s accuracy. In this study, we propose DESPP-DETR, a one-stage detection network for real-time vehicle detection. It is based on bipartite matching and a transformer encoder-decoder architecture, with the addition of a dense connection block and enhanced spatial pyramid pooling. The presence of dense connection block strengthens feature extraction. The enhanced spatial pyramid pooling eliminates the fixed-size constraint and increases the network’s learning capacity. When compared to existing models, DESPP-DETR achieves greater accuracy in real-time vehicle detection. On the MS COCO 2017 dataset, the proposed model achieves an improved mean average precision (mAP) of 75.53%, making it a promising solution for intelligent transportation systems.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: ACM Transactions on Asian and Low-Resource Language Information Processing
Disclaimer: 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.