scholarly journals Generalized Parking Occupancy Analysis Based on Dilated Convolutional Neural Network

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 277 ◽  
Author(s):  
Sherzod Nurullayev ◽  
Sang-Woong Lee
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Traffic Congestion ◽  
Parking Space ◽  
Urban Centers ◽  
Detection Systems ◽  
Perspective Distortion ◽  
Occupancy Detection ◽  
Parking Lot ◽  
Parking Space Detection

The importance of vacant parking space detection systems is increasing dramatically as the avoidance of traffic congestion and the time-consuming process of searching an empty parking space is a crucial problem for drivers in urban centers. However, the existing parking space occupancy detection systems are either hardware expensive or not well-generalized for varying images captured from different camera views. As a solution, we take advantage of an affordable visual detection method that is made possible by the fact that camera monitoring is already available in the majority of parking areas. However, the current problem is a challenging vision task because of outdoor lighting variation, perspective distortion, occlusions, different camera viewpoints, and the changes due to the various seasons of the year. To overcome these obstacles, we propose an approach based on Dilated Convolutional Neural Network specifically designed for detecting parking space occupancy in a parking lot, given only an image of a single parking spot as input. To evaluate our method and allow its comparison with previous strategies, we trained and tested it on well-known publicly available datasets, PKLot and CNRPark + EXT. In these datasets, the parking lot images are already labeled, and therefore, we did not need to label them manually. The proposed method shows more reliability than prior works especially when we test it on a completely different subset of images. Considering that in previous studies the performance of the methods was compared with well-known architecture—AlexNet, which shows a highly promising achievement, we also assessed our model in comparison with AlexNet. Our investigations showed that, in comparison with previous approaches, for the task of classifying given parking spaces as vacant or occupied, the proposed approach is more robust, stable, and well-generalized for unseen images captured from completely different camera viewpoints, which has strong indications that it would generalize effectively to other parking lots.

scholarly journals Parking Space Tracker Using Image Visualization

2021 ◽  
pp. 396-403
Author(s):  
Prof. Pradnya Kasture ◽  
Purva Hattale ◽  
Vikrant Jangam ◽  
Shrutika Khilare ◽  
Yash Ratnaparkhi
Keyword(s):  
Image Processing ◽  
Wireless Communication ◽  
Mobile Phone ◽  
Parking Space ◽  
Image Visualization ◽  
Occupancy Detection ◽  
Parking Lot ◽  
Parking Space Detection ◽  
Modern Era

In modern era, the trouble of parking is also growing because of the growth within side the quantity of vehicles. From the closing decade, there are numerous researches took place with an goal to broaden a really perfect automated parking slot occupancy detection. There is an auto mechanism that can park vehicle automatically but it is required to detect which parking slot is available and which one is busy. In this paper propose a parking space detection using image processing. In this paper proposes parking-space occupancy detection, Visualization of free parking spaces, Parking statistics, Wireless communication, Easily available components, System will get Live-stream video of the parking lot from camera. Images are captured when a car enters or leaves the parking lot. System will also work in Mobile phone (Browser).

scholarly journals Strategi Penanganan Kemacetan Arus Lalu Lintas Berdasarkan Persepsi Masyarakat

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Hilma Erliana
Keyword(s):  
Traffic Congestion ◽  
Public Perception ◽  
Market Area ◽  
Parking Space ◽  
The Road ◽  
Traffic Jams ◽  
Parking Lot ◽  
Parking Management ◽  
The City ◽  
Banda Aceh

Abstrak: Pasar sangatlah identik dengan pusat keramaian, karena pasar merupakan pusat perdagangan yang terletak di pusat kota yang sering disebut juga dengan kawasan Central Bussiness District (CBD). Karena pasar merupakan pusat keramaian sehingga mengundang banyak masyarakat untuk datang ke kawasan tersebut sehingga menimbulkan kemacetan arus lalu lintas, akibat tidak terkendalinya arus lalu lintas di sekitar pasar. Kemacetan arus lalu lintas salah satunya akibat dari tidak terkendalinya parkir sehingga membuat kondisi parkir di sekitar daerah tersebut menjadi semakin semrawut. Pasar Aceh merupakan salah satu pusat perdagangan terbesar dan yang paling berkembang di kota Banda Aceh. Semakin berkembangnya kawasan Pasar Aceh membuat aktivitas pergerakan masyarakat semakin meningkat, sehingga membuat menurunnya tingkat pelayanan jalan di kawasan tersebut,  yang diakibatkan oleh kendaraan yang berjalan lambat, parkir, pejalan kaki dan pedagang kaki lima. Pasar Aceh memiliki fasilitas ruang parkirnya. Setelah disediakan ruang parkir pada gedung Pasar Aceh (off-street parking) yang baru ini ternyata masih banyak masyarakat yang lebih memilih parkir dibadan jalan (on-street parking), sementara pelataran parkir pada gedung Pasar Aceh belum habis terisi penuh, sehingga masih terjadi kemacetan dan kesemrawutan lalu lintas yang diakibatkan oleh banyaknya pengguna jalan yang menggunakan badan jalan sebagai tempat parkir. Tujuan penelitian ini adalah : (1) mengevaluasi kinerja jalan yang terbebani oleh kendaraan yang melintasi jalan di kawasan Pasar Aceh, (2) mengevaluasi kebutuhan ruang parkir yang optimal untuk melayani para pengunjung Pasar Aceh dan (3) mengidentifikasi permasalahan-permasalahan yang timbul akibat on-street parking berdasarkan persepsi masyarakat. Sehingga diharapkan dapat direncanakan manajemen parkir yang baik untuk masa yang akan datang.Kata kunci : tingkat pelayanan jalan, permasalahan parkir, persepsi masyarakat, manajemen parkirAbstract: The market is very synonymous with the center of the crowd, because the market is a trading center located in the city center which is often referred to as the Central Bussiness District (CBD). Because the market is a center of crowds that invites many people to come to the area, causing traffic congestion, due to uncontrolled traffic flow around the market. One of the traffic jams is due to uncontrolled parking, making parking conditions around the area increasingly chaotic. Aceh Market is one of the largest and most developed trade centers in the city of Banda Aceh. The increasing development of the Aceh Market area has made community movement activities increasingly increasing, which has caused a decline in the level of road services in the area, which is caused by vehicles that run slowly, parked, pedestrians and street vendors. Aceh Market has its parking facilities. Having provided parking space on the new Pasar Aceh (off-street parking) building it turns out that there are still many people who prefer on-street parking, while the parking lot on the Aceh Market building has not been fully filled, so traffic jams still occur and the chaos of traffic caused by many road users who use the road as a parking lot. The objectives of this study are: (1) evaluating road performance burdened by vehicles crossing the road in the Aceh Market area, (2) evaluating optimal parking space requirements to serve Aceh Market visitors and (3) identifying problems arising from -street parking based on people's perception. So it is expected that good parking management can be planned in the future. Keywords: level of road service, parking problems, public perception, parking management

scholarly journals Deteksi Dini Kanker Kulit menggunakan K-NN dan Convolutional Neural Network

2020 ◽  
Vol 7 (2) ◽  
pp. 373
Author(s):  
Teresia R. Savera ◽  
Winsya H. Suryawan ◽  
Agung Wahyu Setiawan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Skin Cancer ◽  
Convolutional Neural Network ◽  
Learning Algorithm ◽  
Detection Systems ◽  
Deep Learning Algorithm ◽  
Artificial Neural ◽  
Processing Techniques ◽  
Further Development

<p>Kanker kulit adalah salah satu jenis kanker yang dapat menyebabkan kematian sehingga diperlukan sebuah aplikasi perangkat lunak yang dapat digunakan untuk membantu melakukan deteksi dini kanker kulit dengan mudah. Sehingga diharapkan deteksi dini kanker kulit dapat terdeteksi lebih cepat. Pada penelitian ini terdapat dua metode yang digunakan untuk melakukan deteksi dini kanker kulit yaitu deteksi dengan klasifikasi secara regresi dan <em>artificial neural network</em> dengan arsitektur <em>convolutional neural network</em>. Akurasi yang diperoleh dengan menggunakan klasifikasi secara regresi adalah sebesar 75%. Sementara, akurasi deteksi yang didapatkan dengan menggunakan <em>convolutional neural network</em> adalah sebesar 76%. Hasil yang diperoleh dari kedua metoda ini masih dapat ditingkatkan pada penelitian lanjutan, yaitu dengan cara melakukan prapengolahan pada set data citra yang digunakan. Sehingga set data yang digunakan memiliki tingkat pencahayaan, sudut (pengambilan), serta ukuran citra yang sama. Apabila tersedia sumber daya komputasi yang besar, akan dilakukan penambahan jumlah citra yang digunakan, baik itu sebagai set data latih maupun uji.</p><p> </p><p><em><strong>Abstract</strong></em></p><p class="Abstract"><em>Skin cancer is one type of cancer that can cause death for many people. Because of this, an application is needed to easily detect skin cancer early that the cancer can be handled with more quickly. In this study there were two methods used to detect skin cancer, namely detection by regression classification and detection by classifying using artificial neural networks with network convolutional architecture. Detection with regression classification gives an accuracy of 75%. While detection using convolutional neural networks gives an accuracy of 76%. These proposed early detection systems can be improved to increase the accuracy. For further development, several image processing techniques will be applied, such as contrast enhancement and color equalization. For future works, if there is more computational resource, more images can be used as dataset and implement the deep learning algorithm to improve the accuracy.</em></p><p><em><strong><br /></strong></em></p>

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