Driver’s Seat Belt Detection Using CNN
| Autor: | D. S. Bhupal Naik, G Sai Lakshmi, V Ramakrishna Sajja, J. Nageswara Rao, D. Venkatesulu |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Artificial neural network
Computer science business.industry General Mathematics Word error rate Pattern recognition Convolutional neural network Education law.invention Convolution Support vector machine Computational Mathematics Statistical classification Computational Theory and Mathematics law Seat belt Preprocessor Artificial intelligence business |
| Zdroj: | Turkish Journal of Computer and Mathematics Education (TURCOMAT). 12:776-785 |
| ISSN: | 1309-4653 |
| DOI: | 10.17762/turcomat.v12i5.1483 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|