Self-Supervised Feature Enhancement Networks for Small Object Detection in Noisy Images

Recent CNN-based approaches have shown impressive improvements in object detection, but detecting small objects in images is still a challenging task. Small object detection becomes more difficult if the image contains a lot of noise, which is frequent in real environments. The main reason is that the ratio of visual signal to noise on small objects is very low, making it difficult to extract rich features for detection. To address this issue, we propose a feature enhancement network (FEN) that is trained in a self-supervised manner. Specifically, FEN takes features from input images whose values randomly were erased, then predicts the erased values by aggregating neighboring values. This scheme enables FEN to improve features using surrounding values, which have great effects on enriching features from small-object regions during the test phase. To verify the robustness of our method against small object detection from noisy images, we adopt vehicle detection in aerial images as the main target task. The proposed method consistently outperformed the baseline methods in our experiments. We further present a variety of empirical studies, quantitatively and qualitatively, for in-depth analysis.