YOLOv8n-CGW: A novel approach to multi-oriented vehicle detection in intelligent transportation systems
Multimedia Tools and Applications, cilt.84, sa.7, ss.3809-3840, 2025 (Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 84 Sayı: 7
- Basım Tarihi: 2025
- Doi Numarası: 10.1007/s11042-024-19145-4
- Dergi Adı: Multimedia Tools and Applications
- Derginin Tarandığı İndeksler: Scopus, ABI/INFORM, Compendex, INSPEC, zbMATH
- Sayfa Sayıları: ss.3809-3840
- Anahtar Kelimeler: Deep neural network, Pre-processing, Transfer learning, Vehicle detection
- İstanbul Gelişim Üniversitesi Adresli: Hayır
Özet
In the context of Intelligent Transportation Systems (ITS), the role of vehicle detection and classification is indispensable for streamlining transportation management, refining traffic control, and conducting in-depth accident analyses. However, the intricate task of accurately detecting multi-oriented vehicles in diverse scenarios remains a challenge, even with the advancements in ITS. Factors such as vehicle morphology, design, road conditions, structural details, and climatic variables provide a significant part in the detection. To address this, our study introduces an advanced YOLOv8n model, specifically the YOLOv8n-CGW, which is built upon pre-trained deep learning frameworks. This model is meticulously tailored to enhance the detection capabilities for multi-oriented vehicles. Comprehensive evaluations on esteemed datasets like MtV, MERGED, Custom dataset, DAWN, WEDGE, and UA-DETRAC underscore the superiority of our approach over existing models. Notably, our YOLOv8n-CGW model achieves a remarkable mAP of 83.3% and boasts a swift inference time of 0.0019s, setting a new benchmark in the domain.