Hierarchical Object Detection and Classification Using SSD Multi-Loss

Abstract

When merging existing similar datasets, it would be attractive to benefit from a higher detection rate of objects and the additional partial ground-truth samples for improving object classification. To this end, a novel CNN detector with a hierarchical binary classification system is proposed. The detector is based on the Single-Shot multibox Detector (SSD) and inspired by the hierarchical classification used in the YOLO9000 detector. Localization and classification are separated during training, by introducing a novel loss term that handles hierarchical classification in the loss function (SSD-ML). We experiment with the proposed SSD-ML detector on the generic PASCAL VOC dataset and show that additional super-categories can be learned with minimal impact on the overall accuracy. Furthermore, we find that not all objects are required to have classification label information as classification performance only drops from $73.3\%$ to $70.6\%$ while $60\%$ of the label information is removed. The flexibility of the detector with respect to the different levels of details in label definitions is investigated for a traffic surveillance application, involving public and proprietary datasets with non-overlapping class definitions. Including classification label information from our dataset raises the performance significantly from $70.7\%$ to $82.2\%$. The experiments show that the desired hierarchical labels can be learned from the public datasets, while only using box information from our dataset. In general, this shows that it is possible to combine existing datasets with similar object classes and partial annotations and benefit in terms of growth of detection rate and improved class categorization performance.