Front-View Vehicle Make and Model Recognition on Night-Time NIR Camera Images

In this study, we propose a deep learning based vehicle make and model recognition method for weakly illuminated near-infrared camera images (NIR). Unlike earlier approaches that consider color images obtained in well-lit environments, this study proposes an approach for images captured at night-time. In the proposed approach, vehicle localization is carried out using single shot multibox detector (SSD) model. Next, we utilize a convolutional neural network (CNN) based vehicle model classifier on the detected vehicle region. Data sets of different shade, light, reflection and other lighting effects have been created to be used in the training and testing stages of the proposed methods. Vehicle make and model classification model was tested using 3327 real-world night-time NIR images collected on a roadway. In order to observe the performance of the proposed vehicle make-model recognition method on enhanced images, three popular low-light image enhancement methods are also applied to our test dataset. Proposed model achieved 86% accuracy rate in model recognition and 95% accuracy rate in make recognition tasks.