A post-processing pixel-wise ROI mask applied to pretrained YOLO detections improved parking occupancy counting, with YOLOv9e reaching 99.68% balanced accuracy on a custom dataset, though the evaluation metrics are not clearly tied to the labels.
YOLOv1 to YOLOv10: The fastest and most accurate real-time object detection systems
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abstract
This is a comprehensive review of the YOLO series of systems. Different from previous literature surveys, this review article re-examines the characteristics of the YOLO series from the latest technical point of view. At the same time, we also analyzed how the YOLO series continued to influence and promote real-time computer vision-related research and led to the subsequent development of computer vision and language models.We take a closer look at how the methods proposed by the YOLO series in the past ten years have affected the development of subsequent technologies and show the applications of YOLO in various fields. We hope this article can play a good guiding role in subsequent real-time computer vision development.
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Smart Parking with Pixel-Wise ROI Selection for Vehicle Detection Using YOLOv8, YOLOv9, YOLOv10, and YOLOv11
A post-processing pixel-wise ROI mask applied to pretrained YOLO detections improved parking occupancy counting, with YOLOv9e reaching 99.68% balanced accuracy on a custom dataset, though the evaluation metrics are not clearly tied to the labels.