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Smart city article details
| Title | A System For Detecting Automated Parking Slots Using Deep Learning |
|---|---|
| ID_Doc | 5405 |
| Authors | Maharshi R.T.; Nagajyothi D.; Thrishul P.; Reethika P. |
| Year | 2023 |
| Published | 2nd IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2023 |
| DOI | http://dx.doi.org/10.1109/ICDCECE57866.2023.10150637 |
| Abstract | Due to the rapid increase in vehicle numbers without an equal increase in parking spaces, traffic congestion at the parking spaces is a significant issue facing contemporary society. Urban parking in smart cities. Sharing parking space availability data and using the right suggestion techniques can help solve issues. The majority of suggestion schemes suggest finding the closest or most affordable parking space from the perspective of the driver. But such a self-centered action might result in low utilization. According to the government, walking distance can be sacrificed to boost parking space utilization. In the past ten years, outside parking lot emptiness detection systems have generated a lot of interest because of their many useful uses. But one issue that this field's academics commonly run across is the lack of a representative data set. Through the use of Deep Learning and Instance Segmentation algorithms, the research conducted here assists in resolving the problem of road blockage at the primary networking bottleneck, the parking spots. The model collects a list of all initially available parking, processes the data in real-time to ascertain if the spaces are empty or filled by any vehicles, and provides details on the available spaces in the selected area. The model finds suitable parking spaces for two-wheelers in addition to finding a free parking spot for automobiles with fewer occupants. By effectively obtaining a mask recognition rate of over 92.46 and identification rate of 98.5, the recommended system demonstrates increased robustness. © 2023 IEEE. |
| Author Keywords | CNN; MRCNN; Parking System; Segmentation; Vision-Based Vehicle Parking System |
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