Detection parking Spaces by using the ResNet50 Algorithm
DOI:
https://doi.org/10.29304/jqcm.2022.14.2.932Keywords:
deep learning, convolutional networks, parking lots, occupancy detection, ResNet50 architectureAbstract
Finding an empty parking space in a crowded area is very stressful due to congestion. The absence of empty space leads to fatigue before the main activity, increased fuel consumption, which leads to increased pollution, and increased traffic due to the search for empty space. Therefore, it was necessary to have a system that would help drivers know the condition of each parking space. Empty or occupied with a vehicle. Where empty and occupied spaces are calculated and determined. The system displays on the screen at a convenient location in the parking lot a map to guide the driver to vacant positions and a warning if all positions are occupied. All this is done through images taken from the surveillance cameras in the parking lot. We find that the advantages of using vision-based systems over other existing systems are threefold. First There is no need to update the infrastructure of the parking lot, provided that the place is equipped with CCTV cameras that monitor the parking spaces covering the entire place. Secondly, camera-based systems give the exact location i.e. a detailed map of the vacant car parks which are good and necessary vacant lots. Third, camera-based methods are highly applicable in street parking spaces and residential areas.
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References
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