Feature Implicit Enhancement via Super-Resolution for Small Object Detection

Zhehao Xu, Mengyin Liu, Chao Zhu, Fang Zhou, Xu-Cheng Yin

Abstract: In recent years, object detection has made significant strides due to advancements in deep convolutional neural networks. However, the detection performance for small objects remains challenging. The visual information of small objects is easily confused with the background and even more likely to get lost in a series of downsampling operations due to the limited number of pixels, resulting in poor representations. In this paper, we propose a novel approach namely Feature Implicit Enhancement via Super-Resolution (FIESR) to learn more robust feature representations for small object detection. Our FIESR consists of two detection branches and requires two steps of training. Firstly, the detector learns the relationship between low-resolution and corresponding original high-resolution images to enhance the representations of small objects by minimizing a super-resolution loss between the two branches. Secondly, the detector is fine-tuned on original resolution images to fit extremely large objects. Additionally, our FIESR could be applied to various popular detectors such as Faster-RCNN, RetinaNet, FCOS, and DyHead. Our FIESR achieves competitive results on COCO dataset and is proved effective and flexible by extensive experiments.