room for development. It is demonstrable that the
YOLOv8s in this experiment work quite well. The
experimental findings demonstrated that the
YOLOv8s outperforms the YOLOv5s in terms of
performance.
4
CONCLUSION
In contrast to conventional approaches, this study
suggests a camera sensor-based algorithm for
detecting cars and available space for smart parking
projects. It is known that the YOLOv5 and YOLOv8
models have successfully detected cars and available
spaces in parking lot images based on the research
findings that have been described. Variations exist in
parking lot detecting performance metrics. For recall,
mAP 0.5, and mAP 0.5:0.95, the YOLOv8 model
performs better than the YOLOv5 model; the
differences in the values of each performance are
0.8%, 1.6%, and 1.2%. With a 0.5% difference in
accuracy performance value, the YOLOv5 model
outperforms the YOLOv8 model.
We will keep researching camera sensors in-depth
in the future in an effort to meet our target of being
able to recognize objects in different parking lots
more accurately than current detectors as soon as
feasible.
ACKNOWLEDGEMENTS
This work was supported by UTA’45 Jakarta. The
source code for the experiments is available at the
author.
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