# Generalized Parking Occupancy Analysis Based on Dilated Convolutional Neural Network +
## 1 Introduction  
The noteworthy growth of the automotive industry together with a lack of urban planning have caused problems such as traffic congestion, air contamination, and driving difficulties. To facilitate consumers, most car manufacturers provide a pre-installed or aftermarket navigation system in vehicles. This enables drivers to easily navigate to their destination. However, navigation systems provide a rough estimate of the remaining distance to the destination. With the recent technological advancements in sensing and intelligent technologies, companies are interested to find out whether these advancements can assist in reducing the time spent to search for vacant parking spaces. Locating an unoccupied parking space is a major predicament that drivers face since searching it is a tedious process. Thus, systems that can detect vacant parking spaces willenable drivers to efficiently locate vacant parking spaces. Such systems are referred to as, parking guidance systems (PGSs). PGS can decrease the time consumed by drivers in searching for a parking space and help them avoid driving impediments. As a subsidiary effect PGSs relieve overall traffic congestion in city centers, consequently, decrease fuel consumption and CO emissions. The goal of PGS is to obtain the most credible and definitive data from parking vacancies and provide them in a form that is most helpful for consumers. PGSs differ considerably in their detection performance, optimization, and reporting process. Our goal is to improve the detection performance of a PGS. Three principal factors should be considered when selecting a detection scheme. Robustness is the primary factor. A robust indicator should accurately provide the status of a parking space, despite the diversity in environmental parameters, such as temperature, and the varieties of cars and parking slots. Second, maintenance is an additional determinant that must be considered, together with the cost of new tools to be purchased per parking stall, the deployment effort, and the necessity to restrict normal parking throughout the installation period. Third, the number of sensing units clearly has a direct effect on the establishment and maintenance expenses of a detection system. To resolve this problem, researchers have proposed methods can be characterized as counter-, sensor-, and image-based. Counter-based methods compute the number of cars entering and exiting the parking section. Thus, systems based on these methods include gate-arm counters and inductive loop detectors positioned at the gates of parking facilities. The systems' display panels show reports and directions and guide the driver toward a vacant parking space. This type of arrangement can notify the total number of unoccupied spaces; however, it does not guide a driver to the exact location of a vacant parking space. An alternative approach is to use sensors (such as ultrasonic, inductive loop, and infrared laser sensors), which can be pre-installed in each parking space for indicating its occupancy status. These sensors are normally reliable; however, the principal disadvantage of sensor-based methods is the cost of building the system because a great number of sensor units are needed to cover the entire parking area. A vision-based detector can overcome the shortcomings of sensor per-stall and counter-based detection methods. A vision-based system is an optimal choice since its installation is manageable and it demands no closedowns. Moreover, computer vision for traffic applications is an emerging area of research, including notable advances in the last few years. For instance, intelligent transportation management systems, like those investigated in [1], attempt to achieve tasks such as tracking, detection, and identification of vehicles, as well as higher-level assignments like comprehending vehicle behaviors and exposing irregularities. In a vision-based system, every visual node which includes a camera and a transmitter can observe multiple vehicles concurrently, decreasing the cost per parking stall.