Improving Small Object Detection

摘要

While the problem of detecting generic objects in natural scene images has been the subject of research for a long time, the problem of detection of small objects has been largely ignored. While generic object detectors perform well on medium and large sized objects, they perform poorly for the overall task of recognition of small objects. This is because of the low resolution and simple shape of most small objects. In this work, we suggest a simple yet effective upsampling-based technique that performs better than the current state-of-the-art for end-to-end small object detection. Like most recent methods, we generate proposals and then classify them. We suggest improvements to both these steps for the case of small objects.