Visual Parking Occupancy Detection Using Extended Contextual Image Information via a Multi-Branch Output ConvNeXt Network

Abstract

Along with society’s development, transportation has become a key factor in human daily life, increasing the number of vehicles on the streets. Consequently, the task of finding free parking slots in metropolitan areas can be dramatically challenging, increasing the chance of getting involved in an accident and the carbon footprint, and negatively affecting the driver’s health. Therefore, technological resources to deal with parking management and real-time monitoring have become key players in this scenario to speed up the parking process in urban areas. This work proposes a new computer-vision-based system that detects vacant parking spaces in challenging situations using color imagery processed by a novel deep-learning algorithm. This is based on a multi-branch output neural network that maximizes the contextual image information to infer the occupancy of every parking space. Every output infers the occupancy of a specific parking slot using all the input image information, unlike existing approaches, which only use a neighborhood around every slot. This allows it to be very robust to changing illumination conditions, different camera perspectives, and mutual occlusions between parked cars. An extensive evaluation has been performed using several public datasets, proving that the proposed system outperforms existing approaches.

Feature Map Analysis of Neural Networks for the Application of Vacant Parking Slot Detection

“…In addition, feature map visualization was employed to convert the features into visual information by applying normalization. The dataset was acquired in the morning and afternoon to reflect illumination variations, like in other studies [21,25]. The NNs were designed to generate feature maps of the same size to directly compare their features.…”

“…NNs with an outstanding classification performance were needed to obtain the correct feature analysis outcome, and an appropriate dataset was required to make outstanding NNs. Public datasets, such as PKLot [33] and CNRPark [25], have been used in many studies. However, the datasets were inappropriate for our experiments because they comprise parking slot images with small sizes or extensive occlusion caused by lampposts and trees.…”

“…A variation of the ConvNeXT model attains an accuracy of >99% on public datasets [24]. Likewise, many studies have demonstrated that feature extraction using NNs is better for identifying vacant parking slots than feature extraction using conventional methods [20,21,25]. Vacant parking slot detection using NNs is advantageous, in that it can be applied to various situations without considering feature-extraction methods.…”

Rethinking Feature Generalization in Vacant Space Detection

“…They are typically installed above each parking space and can detect changes in the infrared light caused by the presence of a vehicle. Video cameras [ 6 , 7 , 8 , 9 , 10 , 11 ] can be used to detect the presence or absence of a vehicle in a parking space. They are typically installed above each parking space and use image analysis software to detect changes in the video feed caused by the presence of a vehicle.…”