CrossDet: Crossline Representation for Object Detection

Object detection aims to accurately locate and classify objects in an image, which requires precise object representations. Existing methods usually use rectangular anchor boxes or a set of points to represent objects. However, these methods either introduce background noise or miss the continuous appearance information inside the object, and thus cause incorrect detection results. In this paper, we propose a novel anchor-free object detection network, called CrossDet, which uses a set of growing cross lines along horizontal and vertical axes as object representations. An object can be flexibly represented as cross lines in different combinations. It not only can effectively reduce the interference of noise, but also take into account the continuous object information, which is useful to enhance the discriminability of object features and find the object boundaries. Based on the learned cross lines, we propose a crossline extraction module to adaptively capture features of cross lines. Furthermore, we design a decoupled regression mechanism to regress the localization along the horizontal and vertical directions respectively, which helps to decrease the optimization difficulty because the optimization space is limited to a specific direction. Our method achieves consistently improvement on the PASCAL VOC and MS-COCO datasets. The experiment results demonstrate the effectiveness of our proposed method. Code can be available at: https://github.com/QiuHeqian/CrossDet.

Automated summary

This paper introduces a novel anchor-free object detection network, CrossDet, which uses growing cross lines to represent objects, effectively reducing background noise interference and capturing continuous object information. Experimental results demonstrate the effectiveness of the proposed method in improving object detection performance.