Embedded real-time speed limit sign recognition using image processing and machine learning techniques

Made available in DSpace on 2018-11-29T04:54:17Z (GMT). No. of bitstreams: 0 Previous issue date: 2017-12-01Instituto Federal do Ceara (IFCE)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Programa Operacional Regional do Norte (NORTE2020) through Fundo Europeu de Desenvolvimento Regional (FEDER)The number of traffic accidents in Brazil has reached alarming levels and is currently one of the leading causes of death in the country. With the number of vehicles on the roads increasing rapidly, these problems will tend to worsen. Consequently, huge investments in resources to increase road safety will be required. The vertical R-19 system for optical character recognition of regulatory traffic signs (maximum speed limits) according to Brazilian Standards developed in this work uses a camera positioned at the front of the vehicle, facing forward. This is so that images of traffic signs can be captured, enabling the use of image processing and analysis techniques for sign detection. This paper proposes the detection and recognition of speed limit signs based on a cascade of boosted classifiers working with haar-like features. The recognition of the sign detected is achieved based on the optimum-path forest classifier (OPF), support vector machines (SVM), multilayer perceptron, k-nearest neighbor (kNN), extreme learning machine, least mean squares, and least squares machine learning techniques. The SVM, OPF and kNN classifiers had average accuracies higher than 99.5 %; the OPF classifier with a linear kernel took an average time of 87 mu s to recognize a sign, while kNN took 11,721 ls and SVM 12,595 ls. This sign detection approach found and recognized successfully 11,320 road signs from a set of 12,520 images, leading to an overall accuracy of 90.41 %. Analyzing the system globally recognition accuracy was 89.19 %, as 11,167 road signs from a database with 12,520 signs were correctly recognized. The processing speed of the embedded system varied between 20 and 30 frames per second. Therefore, based on these results, the proposed system can be considered a promising tool with high commercial potential.Inst Fed Fed Educ Ciencia & Tecnol Ceara IFCE, Lab Proc Digital Imagens & Simulacao Computac, Juazeiro Do Norte, Ceara, BrazilUniv Estadual Paulista, Dept Ciencia Comp, Bauru, SP, BrazilUniv Fortaleza, Programa Posgrad Informat Aplicada, Lab Bioinformat, Fortaleza, CE, BrazilUniv Porto, Fac Engn, Inst Ciencia & Inovacao Engn Mecan & Engn Ind, Dept Engn Mecan, Oporto, PortugalUniv Estadual Paulista, Dept Ciencia Comp, Bauru, SP, BrazilInstituto Federal do Ceara (IFCE): PROINFRA/2013Instituto Federal do Ceara (IFCE): PROAPP/2014Instituto Federal do Ceara (IFCE): PROINFRA/2015CNPq: 470501/2013-8CNPq: 301928/2014-2: NORTE-01-0145-FEDER-00002

Aplikasi Penghitung Kecepatan Mobil dengan Akurasi Tinggi Menggunakan Yolo untuk Meminimasi Kecelakaan

One of the causes of accidents is the lack of vigilance among drivers and violations of vehicle speed exceeding the maximum limit. To mitigate these violations, traffic supervision is necessary, especially in accident-prone areas. This research introduces a video-based vehicle speed and license plate detection system developed using YOLOv5-DeepSORT and HyperLPR to address this issue. The system employs YOLOv5 and DeepSORT to detect and track vehicle movements, thereby obtaining the displacement of the vehicle, which serves as a reference for speed detection. HyperLPR, on the other hand, is utilized for license plate recognition. The research adopts an experimental methodology, involving video recording on the Cipali toll road section, which serves as input for the vehicle speed and license plate detection program. The evaluation of vehicle object detection using YOLOv5 yields a precision metric score of 100%. Moreover, the testing of vehicle speed detection reveals an average error percentage of 7.6% compared to the actual values. In terms of license plate detection, an overall character accuracy rate of 91.82% is achieved.Based on these findings, it can be concluded that the developed vehicle speed and license plate detection system exhibit excellent accuracy and could be considered for implementation, taking into account predefined implementation criteria.Keyword: Vehicle speed, vehicle license plate, YOLOv5, DeepSORT, HyperLPR.- Salah satu penyebab terjadinya kecelakaan adalah kurangnya kewaspadaan pengendara dan pelanggaran laju kendaraan melampaui batas maksimal. Untuk mengurangi tindak pelanggaran tersebut diperlukan pengawasan lalu lintas pada area jalan terutama di area yang rawan terjadi kecelakaan.  Dalam penelitian ini dikembangkan sistem deteksi laju dan plat nomor kendaraan berbasis video rekaman menggunakan YOLOv5-DeepSORT dan HyperLPR untuk mengatasi permasalahan tersebut. Dalam sistem ini digunakan YOLOv5 dan DeepSORT untuk mendeteksi dan melacak pergerakan kendaraan sehingga diperoleh perpindahan jarak kendaraan yang digunakan sebagai acuan deteksi laju kendaraan. Adapun HyperLPR digunakan untuk mendeteksi plat nomor dari kendaraan tersebut. Metode yang digunakan dalam penelitian ini yaitu metode experimen dengan melakukan perekaman video pada ruas jalan tol Cipali yang digunakan sebagai input dari program deteksi laju dan plat nomor kendaraan. Hasil pengujian deteksi objek kendaraan menggunakan YOLOv5 diperoleh nilai evaluasi metric precision sebesar 100%. Dan pengujian deteksi laju kendaraan diperoleh nilai rata-rata presentase eror sebesar 7,6% terhadap nilai sebenarnya. Adapun dari deteksi plat nomor kendaraan diperoleh hasil akurasi karakter secara keseluruhan sebesar 91,82 %. Dari hasil tersebut dapat disimpulkan bahwa sistem deteksi laju dan plat nomor kendaraan yang dikembangkan memiliki akurasi yang sangat baik dan dapat dipertimbangkan untuk digunakan dengan memperhatikan beberapa kriteria implementasi yang telah ditentukan. Kata kunci: Laju kendaraan, plat nomor kendaraan, YOLOv5, DeepSORT HyperLPR