SCLNet: A Scale-Robust Complementary Learning Network for Object Detection in UAV Images

TL;DR

This paper introduces SCLNet, a novel scale-robust complementary learning network for UAV object detection, explicitly addressing scale variation and small object detection challenges through two complementary modules and an end-to-end cooperation mechanism.

Contribution

The paper proposes a new scale-robust learning framework with explicit complementary modules and cooperation, improving UAV object detection performance on scale challenges.

Findings

Enhanced detection of small objects in UAV images.

Improved robustness to scale variations.

Superior performance over existing methods.

Abstract

Most recent UAV (Unmanned Aerial Vehicle) detectors focus primarily on general challenge such as uneven distribution and occlusion. However, the neglect of scale challenges, which encompass scale variation and small objects, continues to hinder object detection in UAV images. Although existing works propose solutions, they are implicitly modeled and have redundant steps, so detection performance remains limited. And one specific work addressing the above scale challenges can help improve the performance of UAV image detectors. Compared to natural scenes, scale challenges in UAV images happen with problems of limited perception in comprehensive scales and poor robustness to small objects. We found that complementary learning is beneficial for the detection model to address the scale challenges. Therefore, the paper introduces it to form our scale-robust complementary learning network (SCLNet) in conjunction with the object detection model. The SCLNet consists of two implementations and a cooperation method. In detail, one implementation is based on our proposed scale-complementary decoder and scale-complementary loss function to explicitly extract complementary information as complement, named comprehensive-scale complementary learning (CSCL). Another implementation is based on our proposed contrastive complement network and contrastive complement loss function to explicitly guide the learning of small objects with the rich texture detail information of the large objects, named inter-scale contrastive complementary learning (ICCL). In addition, an end-to-end cooperation (ECoop) between two implementations and with the detection model is proposed to exploit each potential.