A Super-Learner Ensemble of Deep Networks for Vehicle-Type Classification

Automatic vehicle-type classification plays an imperative role in the development of efficient Intelligent Transportation Systems (ITS). In this paper, a super-learner ensemble is proposed for the vehicle-type classification problem. A densely connected single-split super learner is utilized to exploit the strengths and diminish the weaknesses of the individual base learners ResNet50, Xception, and DenseNet. The super learner aims to learn fusion weights in a data-adaptive manner to obtain the optimal combination of the base learners. The proposed method is simple, robust, and enhances the discrimination capabilities among the similarly-looking classes without requiring any hand-crafted features or logical reasoning. The proposed method is evaluated using two of the most challenging publicly available traffic surveillance datasets: the MIOvision Traffic Camera Dataset (MIO-TCD) and the Beijing Institute of Technology's (BIT) vehicle classification dataset. Three variants of the super learner ensemble: RXD-CV-CW, RXD-CV-CW-NCW and Augmented-RXD, were examined on the MIO-TCD dataset with variations in applying class weights and data augmentation during training. RXD-CV-CW-NCW and Augmented-RXD share the third place among the published state-of-the-art methods reported in the MIO-TCD classification challenge. Augmented-RXD generalizes to the classes in common between the two datasets without degrading its performance on the MIO-TCD dataset. Both variants achieved an overall accuracy of 97.94%, and a Cohen Kappa score of 96.78%. In addition, the super-learner variants that we trained on the BIT-Vehicle dataset images achieved overall accuracies of up to 97.62%.