Tenancy Status Identification of Parking Slots Using Mobile Net Binary Classifier

Abstract

The inefficiency in accessing the tenancy status of the parking slots is mainly due to the results of irregular parking regulation/management. The effective parking management enables to avoid unwanted traffic jams and unnecessary fuel wastages. So an efficient parking is necessary for the developing smart cities that aim for a better way of living. So the paper uses the Mobile-Net Classifier to sort out the tenancy state of the parking slots in the cities to assist a proper parking regulation with better proficiency and perfect management. The Mobile-Net classifiers are a sort of light weight deep neural networks that help in identifying the parking slots available accurately based on the image mined from the live camera that feeds the status of the parking lot continuously. The mechanism put forth detects the patches of images form the live recorded video perfectly and determines the vacant slots. The laid out model was applied in an outdoor parking area to determine the systems effective working on the terms of detection accuracy, false positive and the false negative, true positive and the true negative rates along with the average speed of the in identifying the parking slots. As test case, two different mobile-Net network set up were compared to evaluate the swift ness in processing and the perfectness in detecting.

Intelligent classification of lung malignancies using deep learning techniques

“…The MobileNet was initially developed as a deep learning model by Andrew G. Howard of Google analysis team for Image Classification and Mobile Vision and uses depthwise separable convolution to deepen the network, and reduce parameters and computation. MobileNet can be an efficient architecture and the structure relies on depthwise separable filters that could be a procedure of factorized convolutions that factorize a regular convolution into a depthwise convolution and a 1 3 1 convolution named as pointwise convolution (Kamel et al, 2020). The pointwise convolution then applies a 1 3 1 convolution to merge the outputs of the depthwise convolution.…”

“…MobileNet can be an efficient architecture and the structure relies on depthwise separable filters that could be a procedure of factorized convolutions that factorize a regular convolution into a depthwise convolution and a 1 × 1 convolution named as pointwise convolution (Kamel et al , 2020). The pointwise convolution then applies a 1 × 1 convolution to merge the outputs of the depthwise convolution.…”

“…Deep learning has been shown to be the most effective approach for medical imaging, feature extraction and object categorization. Many researchers have proposed many sorts of deep learning architectures to classify lung cancer (Pang et al , 2020; Tekade and Rajeswari, 2018; Song et al , 2017; Alsabahi et al , 2018; Jin et al , 2016; Huang et al , 2013; Felix et al , 2016; National Lung Screening Trial Research Team, 2011; Kuruvilla and Gunavathi, 2014; Hua et al , 2015; Penedo et al , 1998; Bhaskar and Ganashree, 2021; Rehman et al , 2021; HerbstRs, 2018; Mikołajczyk and Grochowski, 2018; Aisner et al , 2018; Wang et al , 2019; Ran et al , 2021; Kamel et al , 2020; Subetha et al , 2021; Wan et al , 2019; Thomas et al , 2019; Dhiman et al , 2021; Zhang, 2018; Lakshmanaprabu et al , 2019; Monkam et al , 2019; Alakwaa et al , 2017; Rossetto and Zhou, 2017; Fayemiwo et al , 2022).…”

Investigation on Motion Teaching Video Compression Algorithm Based on Artificial Intelligence

“…However, when selecting representative feature values with a large dynamic range or which can well reflect the long amount of information required, it is only selected for compression operations. Therefore, when selecting video content, it is also necessary to consider robustness to improve the quality of compression coding [3][4].…”