Vehicle classification using ResNets, localisation and spatially-weighted pooling

We investigate whether ResNet architectures can outperform more traditional Convolutional Neural Networks on the task of fine-grained vehicle classification. We train and test ResNet-18, ResNet-34 and ResNet-50 on the Comprehensive Cars dataset without pre-training on other datasets. We then modify the networks to use Spatially Weighted Pooling. Finally, we add a localisation step before the classification process, using a network based on ResNet-50. We find that using Spatially Weighted Pooling and localisation both improve classification accuracy of ResNet50. Our method achieves higher accuracy than a range of methods including those that use traditional CNNs. However, our method does not perform quite as well as pre-trained networks that use Spatially Weighted Pooling. Keywords: Vehicle recognition, Intelligent surveillance, ResNets 1. Introduction In the fine-grained vehicle classification problem, a class consists of both make and model attributes, with the optional addition of the year that a particular model version was released (e.g. If such a'year' attribute is required, the difficulty of the problem increases significantly, due to the similarity of updated models. This problem differs from more coarse recognition, which may categorise by vehicle type (car, van, bus, etc) and have far fewer classes. Several methods have been used to try and solve fine-grained vehicle classification. The main limitation of these approaches is the inability to differentiate between similar car models.