An essential task in computer vision - object detection, involves identifying and locating objects within images and/or video frames and has seen significant advancements through models like YOLO (You Only Look Once). This study presents a comparative study of object detection models trained on a custom dataset consisting of auto-rickshaws and license plates. Utilizing the YOLO models - YOLOv8, YOLOv9, and YOLOv10, the study evaluates the performance of their various versions in recognizing and localizing objects. Each model was trained under identical conditions to ensure an unbiased comparison and the results were analyzed based on performance metrics such as precision, recall, mean average precision, and model complexity. The results highlight that the YOLOv10 models - the medium(YOLOv10m) and the balanced(YOLOv10b) achieve better results with the former achieving a mAP@50 value of 0.791 and a F1 score of 0.768 for auto-rickshaw detection. The latter achieved a mAP@50 value of 0.739 and a F1 score of 0.731 for license plate detection with fewer parameters(YOLOv10m - 16.49M and YOLOv10b - 20.45M) with respect to the other models considered. Based on this, the use of YOLOv10m and the YOLOv10b models for future research in object detection tasks is recommended.