Abstract
Abstract: To cut down on occlusion-induced false detections of vehicle targets, an improved vehicle detection strategy based on a more advanced YOLO network is proposed. The proposed method makes use of the Flip-Mosaic algorithm to enhance the network's perception of small targets. A multi-type vehicle target dataset was developed using data from a variety of scenarios. The dataset served as the foundation for the detection model's training. Experiments showed that the Flip-Mosaic data enhancement algorithm reduced false detection rates and improved vehicle detection accuracy.
Sign-in/Register to access full text options 
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 callMore From: International Journal for Research in Applied Science and Engineering Technology
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.
