TY  - JOUR
PY  - 2022//
TI  - Deep learning-based vehicle classification for low quality images
JO  - Sensors (Basel)
A1  - Tas, Sumeyra
A1  - Sari, Ozgen
A1  - Dalveren, Yaser
A1  - Pazar, Senol
A1  - Kara, Ali
A1  - Derawi, Mohammad
SP  - e4740
EP  - e4740
VL  - 22
IS  - 13
N2  - This study proposes a simple convolutional neural network (CNN)-based model for vehicle classification in low resolution surveillance images collected by a standard security camera installed distant from a traffic scene. In order to evaluate its effectiveness, the proposed model is tested on a new dataset containing tiny (100 × 100 pixels) and low resolution (96 dpi) vehicle images. The proposed model is then compared with well-known VGG16-based CNN models in terms of accuracy and complexity. <br><br>RESULTS indicate that although the well-known models provide higher accuracy, the proposed method offers an acceptable accuracy (92.9%) as well as a simple and lightweight solution for vehicle classification in low quality images. Thus, it is believed that this study might provide useful perception and understanding for further research on the use of standard low-cost cameras to enhance the ability of the intelligent systems such as intelligent transportation system applications.<p />  <p>Language: en</p>
LA  - en
SN  - 1424-8220
UR  - http://dx.doi.org/10.3390/s22134740
ID  - ref1
ER  -