Helmet and Number Plate detection of Motorcyclists using Deep Learning and Advanced Machine Vision Techniques

Khan, Fahad Ali; Nagori, Nitin; Naik, Ameya K.

doi:10.1109/icirca48905.2020.9183287

Cited by 12 publications

(5 citation statements)

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“…Dalam real-time pendeteksian objek kecepatan sangat penting dalam pendeteksian objek dikarenakan berbeda pada sebuah gambar, pada suatu vidio dapat mengolah lebih dari 24 frame per second (FPS) atau 24 frame per detik. Jika proses pendeteksian objek terlalu lama maka vidio yang dihasilkan kurang baik, akan mengalami delay pada setiap frame sehingga vidio menjadi patah-patah (Ding et al 2019) Dengan menggunakan pendeteksian objek metode YOLO pada suatu sistem dapat membantu mengklasifikasi setiap jenis kendaraan yang melintas pada jalan raya secara real-time pada rekaman vidio (Khan, Nagori, and Naik 2020) . Jenis kendaraan yang melintas akan terdeteksi otomatis berdasarkan nilai hasil tingkat akurasinya dan klasifikasi.…”

Section: Pendahuluanunclassified

“…Menurut (Khan, Nagori, and Naik 2020) Menurut (Benjelloun et al 2020) dalam penelitiannya yang berjudul " The comparison between two methods of object detection: Fast Yolo model and Delaunay Triangulation " Hasil penelitian pendeteksian memperoleh nilai presisi untuk model pertama 76,13%, dan untuk pendekatan kedua 50%. Jaringan saraf mengalami penyesuaian berlebihan jika kumpulan data terlalu kecil, yang berarti bahwa saya memiliki kinerja yang sangat baik dengan set pelatihan tetapi sangat kinerja yang buruk dengan set tes.…”

Section: Pendahuluanunclassified

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Implementasi Deteksi Real Time Klasifikasi Jenis Kendaraan Di Indonesia Menggunakan Metode YOLOV5

Mulyana¹,

Rofik²

2022

jptam

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Negara Indonesia mempunyai kepadatan penduduk yang sangat padat, terutama dikota-kota besar yang dimana jalan selalu dipadati oleh berbagai jenis kendaran. Pada jam sibuk banyaknya kendaraan yang membuat kemacetan dijalan. Oleh karna itu dibutuhkan pembangunan pelebaran jalan untuk menampung kendaraan yang dipadati oleh berbagai jenis kendaraan yang melintas. Agar pembangunan pelebaran jalan yang tepat pada lokasi yang sering terjadinya kepadatan, maka dibutuhkan sistem pendeteksian jenis-jenis kendaraan yang melintas dijalan raya. Meningkatnya pada macam-macam penelitian tentang pengolahan citra digital diantaranya tentang pendeteksian objek, untuk klasifikasi deteksi jenis kendaraan dijalan raya. Pada penelitian ini penulis membuat sistem pendedeteksi objek memakai metode YOLOV5 untuk mendeteksi jenis kendaraan dijalan raya. Penulis menggunakan dataset sebesar 1332 gambar dengan kelas bajaj, becak, bus, mobil, mobil molen, mobil pik’up, sepedah, sepeda motor, dan truk. Pada hasil penelitian menggunakan metode YOLOV5 yang dapat mengenali objek secara konsisten dengan tingkat akurasi yang cukup tinggi dan memiliki nilai akurasi 90%.

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Section: Pendahuluanunclassified

Implementasi Deteksi Real Time Klasifikasi Jenis Kendaraan Di Indonesia Menggunakan Metode YOLOV5

Mulyana¹,

Rofik²

2022

jptam

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show abstract

“…Whereas the helmet detection [1], [2], & [3] uses deep convolutional neural network, the foreground object in the motorbike identification step is also still obtained using classical thresholding, which would be quite poor in busy scenario. This is suggested in [4] & [5] to be using YOLOv3 [6] method is used to detect the motorcyclists wearing helmet however, no motorbike recognition is verified. In [7] and [8], we initially detected the motorbike & man inside the image using the YOLOv3 method, and afterwards they evaluated the overlapped volume of the grid cell between both the motorbike and man to identify the individual riding the motorbike.…”

Section: Related Work a Helmet Detectionmentioning

confidence: 99%

Detection of License Plate If Helmet Not Found

Minavathi¹

2022

IJRASET

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Motorbike accidents has been growing more in the current years, due to high usage of superbike which is nearly 300- 1200cc, which may reach the speed of 200-300 kmph, in that hyper speed, there are more chances of accidents. In India not only over speeding causes accidents, due to bad roads and road bumps will cause accident even though people riding in low speed. Also, if raider violates the traffic rules cops may try to catch the riders, it might be bit difficult to catch the riders sometime they may escape, it will be too difficult to board a case on them by capturing the License Plate number manually, therefore its highly necessary to build an automated based helmet and license plate recognition based on Deep Learning system using YOLO algorithm

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“…In [29], [30] and [31], although the helmet detection adopts the deep learning method, the traditional background subtraction is still used to obtain the foreground target in the motorcycle detection stage, which will be very poor in the crowded scene. In [32] and [33], it is proposed to use YOLOv3 [34] algorithm to detect whether a motorcyclist is wearing a helmet, but the detection of motorcycles is not reported. In [35] and [36], they first used the YOLOv3 algorithm to detect the motorcycle and person in the picture, and then calculated the overlapping area of the bounding box between the motorcycle and the person to determine the person on the motorcycle.…”

Section: Helmet Detection Based On Deep Learningmentioning

confidence: 99%

“…We find that bicycles, forward-looking tricycles and motorcycles are similar in their riding state. In [32] and [33], they only consider one motorcycle category, which will cause a lot of false detections. Therefore, in order to reduce the false detection rate, we detect three categories at this stage, that is, motorcycle, bicycle and tricycle.…”

Section: Motorcycle Detectionmentioning

confidence: 99%

Real‐time automatic helmet detection of motorcyclists in urban traffic using improved YOLOv5 detector

Jia

Liang

et al. 2021

IET Image Processing

117

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In traffic accidents, motorcycle accidents are the main cause of casualties, especially in developing countries. The main cause of fatal injuries in motorcycle accidents is that motorcycle riders or passengers do not wear helmets. In this paper, an automatic helmet detection of motorcyclists method based on deep learning is presented. The method consists of two steps. The first step uses the improved YOLOv5 detector to detect motorcycles (including motorcyclists) from video surveillance. The second step takes the motorcycles detected in the previous step as input and continues to use the improved YOLOv5 detector to detect whether the motorcyclists wear helmets. The improvement of the YOLOv5 detector includes the fusion of triplet attention and the use of soft-NMS instead of NMS. A new motorcycle helmet dataset (HFUT-MH) is being proposed, which is larger and more comprehensive than the existing dataset derived from multiple traffic monitoring in Chinese cities. Finally, the proposed method is verified by experiments and compared with other state-of-the-art methods. Our method achieves mAP of 97.7%, F1-score of 92.7% and frames per second (FPS) of 63, which outperforms other state-of-the-art detection methods.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Helmet and Number Plate detection of Motorcyclists using Deep Learning and Advanced Machine Vision Techniques

Cited by 12 publications

References 12 publications

Implementasi Deteksi Real Time Klasifikasi Jenis Kendaraan Di Indonesia Menggunakan Metode YOLOV5

Implementasi Deteksi Real Time Klasifikasi Jenis Kendaraan Di Indonesia Menggunakan Metode YOLOV5

Detection of License Plate If Helmet Not Found

Real‐time automatic helmet detection of motorcyclists in urban traffic using improved YOLOv5 detector

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