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@article{204829,
        author = {Greeshma V S},
        title = {A Comparative Benchmark Study of YOLOv8n and YOLOv9t for Indian Vehicle Detection Using the Kaggle Dataset},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {4025-4029},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204829},
        abstract = {Object detection plays a critical role in intelligent transportation systems, particularly in diverse and complex traffic environments such as those found in India. This paper presents a comparative benchmark study of two state-of-the-art object detection models, YOLOv8n and YOLOv9t, evaluated on an Indian vehicle dataset sourced from Kaggle. The primary objective is to assess and compare the detection performance of both models under identical training and evaluation conditions. Both models were trained and tested on the same dataset, and their performances were measured using standard metrics, including mean Average Precision at 0.5 (mAP@0.5), mAP@0.5:0.95, Precision, and Recall. Experimental results demonstrate that YOLOv8n outperforms YOLOv9t across all evaluation metrics, achieving an mAP@0.5 of 0.5485, mAP@0.5:0.95 of 0.3851, Precision of 0.7847, and Recall of 0.4404, compared to YOLOv9t scores of 0.4006, 0.2910, 0.3873, and 0.3815, respectively. The findings suggest that YOLOv8n is a more effective and reliable model for Indian vehicle detection tasks. This study provides valuable insights for researchers and practitioners working on vehicle detection systems tailored to Indian road conditions.},
        keywords = {YOLOv8, YOLOv9, Object Detection, Indian Vehicle Detection, Benchmark Study, mAP, Deep Learning},
        month = {June},
        }