Hierarchical objectness network for region proposal generation and object detection

Abstract Recent region proposal generation methods show a low Intersection-of-Union with the ground-truth boxes. Because they simply regress the coordinates of the bounding boxes by exploiting the single-layer output of convolutional neural networks. This paper proposes a hierarchical objectness network for region proposal generation and object detection to address the inaccurate localization problem. Instead of regressing the coordinates, we subtly localize the objects by predicting the stripe objectness, i.e., a group of probabilities reflecting the existence of the object in each location of the candidate proposal. Additionally, we construct the hierarchical features by reversely connecting multiple convolutional layers to detect objects with large-scale variations. Our experimental results demonstrate that our method performs better than the state-of-the-art region proposal generation methods in terms of recall. Moreover, by integrating with advanced object detection frameworks, our method achieves superior object detection results.