Abstract
Seat belt detection is one of the necessary task which are required in transportation system to reduce accidents due to abrupt stop or high speed accident with other vehicles. In this paper, a technique is proposed to detect whether the driver wears seat belt or not by using convolution neural networks. Convolution Neural Network is nothing but deep Neural Network. ConvNet automatically collects features using filters or kernels from images without human involvement to classify the output images. Compared to different classification algorithms, preprocessing required in ConvNet is least. In this proposed method, first ConvNet is built and trained using Seatbelt dataset of both standard and non-standard. ConvNet learns the features from the images of seat belt dataset and performed better with an accuracy of 91.4% over SVM with 87.17% and an error rate of 8.55% when compared with SVM with 12.83% in case of standard dataset.
Highlights
Passengers and drivers may be injured with the aid of a unexpected halt or a great velocity crash in a vehicle[8][13]
Seat belt detection method has been proposed. It detects whether the driver wear belt or not using Convolution neural network
Seat belt detection Dataset includes two datasets called standard dataset containing the 2155 images which took from Yawning Detection Dataset [7][10], Non standard dataset consists of 8058 images which are downloaded from online sites including belt and no belt images of various drivers .This is divided into two parts known as train set and test set further divided into two parts and named as belt and no belt images
Summary
Passengers and drivers may be injured with the aid of a unexpected halt or a great velocity crash in a vehicle[8][13] To limit these damages, disaster management section need that all motorists and travelers wear seat belts, which are designed as a shield and reduce deadly injuries. Convolution Layer to learn features using convolution operation between input image and filter and pooling layer which is gradually reduce the spatial dimension of the feature map to minimize the no of parameters and computation in network which leads to decrease of over fitting. These features are utilized to classify that the driver wear belt or not. Seat belt detection dataset has images of driver both belt and no belt
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