DDBN: Dual detection branch network for semantic diversity predictions

Abstract

It is well known that detail features and context semantics are conducive to improving object detection performance. However, the current single-prediction detectors do not well incorporate these two types of information together. To alleviate the limitation of single-prediction on the use of multiple types of information, we propose a dual detection branch network (DDBN) with adjacent feature compensation and customized training strategy for semantic diversity predictions. Different from the conventional single-prediction models, our DDBN is in the form of a single model with dual different semantic predictions. In particular, two types of adjacent feature compensations are designed to extract detail and context information from different perspectives. Also, a specialized training strategy is customized for our DDBN to well explore the diversity of predictions for improving the performance of object detection. We conduct extensive experiments on three datasets, i.e., DOTA, MS-COCO, and Pascal-VOC, and the experimental results strongly demonstrate the efficacy of our proposed model.