Learn Discriminative Features for Small Object Detection through Multi-Scale Image Degradation with Contrastive Learning

Abstract

To address challenges such as small target sizes, blurred target features, and difficulty in distinguishing between targets and backgrounds in small object detection, we propose a method based on Multi-Scale Image Degradation combined with the Contrastive Learning model. By leveraging contrastive learning techniques, our approach aims to enhance the discriminative features necessary for accurately distinguishing objects from backgrounds. To specifically target small objects, we subject target samples to various multi-scale image degradation modes before inputting them into the contrastive learning model. Augmentation techniques are then applied to these degraded samples to facilitate effective contrastive feature learning. Consequently, the model is better equipped to uncover the differences between small targets and backgrounds, thereby improving small object detection performance. Furthermore, considering that spatial domain features are sensitive to local changes in the image, while frequency domain features are sensitive to global structural changes, our approach applies the contrastive learning model in both spatial and frequency domains, aiming to acquire more robust features for small object detection. Extensive experiments conducted on the MS COCO dataset and the VisDrone2019 dataset validate the effectiveness of our proposed method in significantly enhancing small object detection accuracy.