Implementasi Framework Yolov7 untuk Kalkulasi Jumlah Tempat Parkir yang Tersedia dengan Menggunakan Citra Digital

Luasnya sebuah lahan parkir pada pusat keramaian seringkali menjadi kendala untuk pengendara mobil mengetahui jumlah ketersediaan termpat parkir yang tersedia. Meskipun saat ini beberapa lahan parkir menerapkan sensor untuk memberikan informasi mengenai ketersediaan tempat parkir, biaya yang dibutuhkan untuk memfasilitasi seluruh lahan parkir dengan sensor relatif mahal. Kamera dapat digunakan sebagai alternatif untuk menggantikan penggunaan sensor parkir, dikarenakan kamera dapat mencakup area yang lebih luas maka biaya yang dibutuhkan menjadi lebih rendah. Pada penelitian kali ini digunakan CNN untuk melakukan deteksi objek dan mengkalkulasi jumlah tempat parkir yang tersedia menggunakan citra digital sebuah lahan parkir. YOLOv7 digunakan untuk melakukan ekstraksi fitur dan klasifikasi fitur dari citra digital. Varian YOLOv7 yang digunakan pada penelitian ini adalah YOLOv7 dan YOLOv7-tiny. Masing-masing varian model dilatih menggunakan dua dataset yang disediakan oleh UNIVERSIDADE FEDERAL DO PARANA´ (Federal University of Parana) dan new-workspace-uxdze yang diunggah pada situs Roboflow. Model-model yang dilatih menggunakan dataset pertama mengalami overfit pada saat dievaluasi. Model YOLOv7 yang dilatih menggunakan dataset yang disediakan oleh new- workspace-uxdze memiliki performa deteksi objek terbaik dengan nilai [email protected] sebesar 91%. Sistem yang dihasilkan pada penlitian ini berupa sebuah Bot Telegram yang dapat menerima perintah dan memberikan informasi mengenai ketersediaan tempat parkir pada citra yang sudah diproses oleh model

A Visual Sensor Network for Parking Lot Occupancy Detection in Smart Cities

Technology is quickly revolutionizing our everyday lives, helping us to perform complex tasks. The Internet of Things (IoT) paradigm is getting more and more popular and is key to the development of Smart Cities. Among all the applications of IoT in the context of Smart Cities, real-time parking lot occupancy detection recently gained a lot of attention. Solutions based on computer vision yield good performance in terms of accuracy and are deployable on top of visual sensor networks. Since the problem of detecting vacant parking lots is usually distributed over multiple cameras, adhoc algorithms for content acquisition and transmission are to be devised. A traditional paradigm consists in acquiring and encoding images or videos and transmitting them to a central controller, which is responsible for analyzing such content. A novel paradigm, which moves part of the analysis to sensing devices, is quickly becoming popular. We propose a system for distributed parking lot occupancy detection based on the latter paradigm, showing that onboard analysis and transmission of simple features yield better performance with respect to the traditional paradigm in terms of the overall rate-energy-accuracy performance

Анализ алгоритмов обработки динамических изображений для интеллектуальных систем мониторинга автомобильных стоянок

Рассматривается классификация современных методов обнаружения свободных мест на автостоянках. Представлена общая модель построения алгоритмов классификации парковочных мест на свободные и занятые, приведены характеристики современных алгоритмов мониторинга статусов парковочных мест

Vacant Parking Lot Information System Using Transfer Learning and IoT

Parking information systems have become very important, especially in metropolitan areas as they help to save time, effort and fuel when searching for parking. This paper offers a novel low-cost deep learning approach to easily implement vacancy detection at outdoor parking spaces with CCTV surveillance. The proposed method also addresses issues due to perspective distortion in CCTV images. The architecture consists of three classifiers for checking the availability of parking spaces. They were developed on the TensorFlow platform by re-training MobileNet (a pre-trained Convolutional Neural Network (CNN)) model using the transfer learning technique. A performance analysis showed 88% accuracy for vacancy detection. An end-to-end application model with Internet of Things (IoT) and an Android application is also presented. Users can interact with the cloud using their Android application to get real-time updates on parking space availability and the parking location. In the future, an autonomous car could use this system as a V2I (Vehicle to Infrastructure) application in deciding the nearest parking space

Forecasting Parking Lots Availability: Analysis from a Real-World Deployment

Smart parking technologies are rapidly being deployed in cities and public/private places around the world for the sake of enabling users to know in real time the occupancy of parking lots and offer applications and services on top of that information. In this work, we detail a real-world deployment of a full-stack smart parking system based on industrial-grade components. We also propose innovative forecasting models (based on CNN-LSTM) to analyze and predict parking occupancy ahead of time. Experimental results show that our model can predict the number of available parking lots in a ±3% range with about 80% accuracy over the next 1-8 hours. Finally, we describe novel applications and services that can be developed given such forecasts and associated analysis