Summary of Smart Parking with Pixel-wise Roi Selection For Vehicle Detection Using Yolov8, Yolov9, Yolov10, and Yolov11, by Gustavo P. C. P. Da Luz et al.
Smart Parking with Pixel-Wise ROI Selection for Vehicle Detection Using YOLOv8, YOLOv9, YOLOv10, and YOLOv11
by Gustavo P. C. P. da Luz, Gabriel Massuyoshi Sato, Luis Fernando Gomez Gonzalez, Juliana Freitag Borin
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers introduce a novel approach for efficient parking management systems using deep learning concepts and Internet of Things (IoT) technology. The proposed system integrates YOLO models, particularly YOLOv8, YOLOv9, YOLOv10, and YOLOv11, with Region of Interest (ROI) selection for object detection to count vehicles in parking lot images. By exploring both edge and cloud computing, the authors found that inference times ranged from 1 to 92 seconds, depending on the hardware and model version. The proposed system achieved a balanced accuracy of 99.68% on a custom dataset of 3,484 images, offering a cost-effective smart parking solution that ensures precise vehicle detection while preserving data privacy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re in a big city and need to find an empty parking spot. This paper is about making it easier to do just that using special computer algorithms called YOLO models. These models can help count the number of cars in a parking lot by looking at pictures taken from cameras or sensors. The researchers came up with a new way to use these models, combining them with other technologies like the Internet of Things (IoT) and edge computing. They found that this approach worked well, with an accuracy rate of almost 99.7%. This could help cities manage parking more efficiently and reduce congestion. |
Keywords
» Artificial intelligence » Deep learning » Inference » Object detection » Yolo
