Classification of Motorcyclists not Wear Helmet on Digital Image with Backpropagation Neural Network

Sutikno, Sutikno; Waspada, Indra; Bahtiar, Nurdin; Sasongko, Priyo Sidik

doi:10.12928/telkomnika.v14i3.3486

Cited by 11 publications

(8 citation statements)

References 8 publications

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“…On the other hand, results on Deep Learning architectures are raising the interest of the research community (see section V). Sutikno et al [49] report a classification process of motorcyclists wearing or not helmets, from images captured on the highway using a backpropagation neural network. It is not clear how the network architecture is defined.…”

Section: A Discriminative Classifiersmentioning

confidence: 99%

“…The images were recorded during several months (spring, summer, fall) during daytime and under good and medium weather conditions. Specially oriented to motorcycles detection and classification and in some cases tracking, most authors present results working with their own datasets that are seldom made public as in [20], [21], [18], [25], [34], [35], [13], [14], [28], [37], [39], [36], [42], [43], [44], [45], [49] and [53]. This is a significant problem to compare results.…”

Section: A Datasetsmentioning

confidence: 99%

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Detection of Motorcycles in Urban Traffic Using Video Analysis: A Review

Espinosa

Velastín

Branch

2021

IEEE Trans. Intell. Transport. Syst.

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Motorcycles are Vulnerable Road Users (VRU) and as such, in addition to bicycles and pedestrians, they are the traffic actors most affected by accidents in urban areas. Automatic video processing for urban surveillance cameras has the potential to effectively detect and track these road users. The present review focuses on algorithms used for detection and tracking of motorcycles, using the surveillance infrastructure provided by CCTV cameras. Given the importance of results achieved by Deep Learning theory in the field of computer vision, the use of such techniques for detection and tracking of motorcycles is also reviewed. The paper ends by describing the performance measures generally used, publicly available datasets (introducing the Urban Motorbike Dataset (UMD) with quantitative evaluation results for different detectors), discussing the challenges ahead and presenting a set of conclusions with proposed future work in this evolving area.

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Section: A Discriminative Classifiersmentioning

confidence: 99%

Section: A Datasetsmentioning

confidence: 99%

Detection of Motorcycles in Urban Traffic Using Video Analysis: A Review

Espinosa

Velastín

Branch

2021

IEEE Trans. Intell. Transport. Syst.

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“…Neural networks (NN) such as the Multilayer Perceptron (MLP) have been proposed for motorcycle detection and classification, even though their architectures require tuning of many parameters and the implemented loss function may not converge to a local optimum. Nevertheless, NN are used for helmet detection in [16,31]. There is also Fuzzy neural network (FNN) [24], but without a significant number of motorcycles to detect in their dataset.…”

Section: Motorcycle Detectionmentioning

confidence: 99%

EspiNet V2: a region based deep learning model for detecting motorcycles in urban scenarios

Oviedo

Velastín

Bedoya

2019

DYNA

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This paper presents “EspiNet V2” a Deep Learning model, based on the region-based detector Faster R-CNN. The model is used for the detection of motorcycles in urban environments, where occlusion is likely. For training, two datasets are used: the Urban Motorbike Dataset (UMD-10K) of 10,000 annotated images, and the new SMMD (Secretaría de Movilidad Motorbike Dataset), of 5,000 images captured from the Traffic Control CCTV System in Medellín (Colombia). Results achieved on the UMD-10K dataset reach 88.8% in average precision (AP) even when 60% motorcycles were occluded, and the images were captured from a low angle and a moving camera. Meanwhile, an AP of 79.5% is reached for SSMD. EspiNet V2 outperforms popular models such as YOLO V3 and Faster R-CNN (VGG16 based) trained end-to-end for those datasets

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“…Figure 9 shows the progress of neural network training and the result is shown through nntools along with the training performance. The number of iteration can be classified if the neural network is a fast learner or slow learner whereas the lesser the number of iteration, the lesser time taken the machine to finish the training [26]. Figure 10 shows training performance graph of epoch versus mean squared error (MSE).…”

Section: Interfacementioning

confidence: 99%

An accurate pattern classification for empty fruit bunch based on the age profile of oil palm tree using neural network

Aziz¹,

Kasno²,

Kamarudin³

et al. 2019

IJECE

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<span lang="EN-US">This paper proposes an efficient method for pattern classification system of empty fruit bunch (EFB) by using a neural network technique. The main advantage of this method is the accuracy and speed of algorithm such that it can be computed rapidly with the proposed system. To test the effectiveness of the proposed method, 120 of EFB’s data with different ages and length that been obtained from Malaysian Palm Oil Board (MPOB) are use in the pattern classification process. In addition, there are three classes of EFB in this system, which are Class 1 (less than 7 year old), Class 2 (8 to 17 year old) and Class 3 (more than 17 year old). It is envisaged that the proposed method is very useful in classifying the EFB and 90% of the sample parameters are successfully classified to its class.</span>

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Classification of Motorcyclists not Wear Helmet on Digital Image with Backpropagation Neural Network

Abstract: One of the world's leading causes of death is traffic accidents. Data from World Health

Cited by 11 publications

References 8 publications

Detection of Motorcycles in Urban Traffic Using Video Analysis: A Review

Detection of Motorcycles in Urban Traffic Using Video Analysis: A Review

EspiNet V2: a region based deep learning model for detecting motorcycles in urban scenarios

An accurate pattern classification for empty fruit bunch based on the age profile of oil palm tree using neural network

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