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%.