Feature Enhancement and Reconstruction for Small Object Detection

Due to the small size and noise interference, small object detection is still a challenging task. The previous work can not effectively reduce noise interference and extract representative features of the small object. Although the upsampling network can alleviate the loss of features by enlarging feature maps, it can not enhance semantics and will introduce more noises. To solve the above problems, we propose CAU (Content-Aware Upsampling) to enhance feature representation and semantics of the small object. Moreover, we propose CSA (Content-Shuffle Attention) to reconstruct robust features and reduce noise interference using feature shuffling and attention. Extensive experiments verify that our proposed method can improve small object detection by 2.2% on the traffic sign dataset TT-100K and 0.8% on the object detection dataset MS COCO compared with the baseline model.