International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Vehicle Detection Based on Improved YOLOv13 with a Dual-Domain Attention Mechanism |
|---|---|
| ABSTRACT | This journal paper presents a novel approach for vehicle detection by integrating an improved version of the YOLOv13 object - detection algorithm with advanced attention mechanisms. The proposed method aims to enhance the accuracy and efficiency of vehicle detection in various real - world scenarios, such as traffic monitoring, autonomous driving, and intelligent transportation systems. By leveraging the strengths of the improved YOLOv13 architecture and attention - based feature enhancement, the algorithm can effectively handle vehicle scale variations, complex backgrounds, and occlusions. Experimental results on standard vehicle detection datasets demonstrate that the proposed method outperforms existing state - of - the - art methods in terms of detection accuracy and speed. |
| TITLE | |
| AUTHOR | QI WANG, RAOUDA AYA Masters Student, Dept. of Computer Science, Nanjing University of Information Science and Technology NUIST, Nanjing, China School of Computer Science, School of Cyber Science and Engineering, NUIST, Nanjing, China |
| VOLUME | 182 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1403002 |
| pdf/2_Vehicle Detection Based on Improved YOLOv13 with a Dual-Domain Attention Mechanism.pdf | |
| KEYWORDS | |
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