NIRNet: Noise Incentive Robust Network in Remote Sensing Object Detection Under Cloud Corruption

Within remote sensing images, complex atmospheric environments commonly bring about distinct variations in imaging visibility and ambient occlusions, significantly transforming the appearance of objects. Nevertheless, modern detectors generally struggle to maintain promising accuracy when encountering realistic scenarios. Devoting to alleviating the issues, we develop a noise incentive robust network (NIRNet) for remote sensing object detection under cloud corruption without relying on hazy images for training. The proposed NIRNet preserves discriminative representations and calibrates them using an incentive mechanism. First, we design a noise perception module (NPM) to deal with diverse cloud corruption types, which generates pointwise calibration weights dependent on the perceived discrepancy between objects and environmental noise. Second, aiming to detect difficult-to-discern objects thoroughly, a dual-path incentive calibration (DPIC) strategy is proposed to combine intensity and stability features weighted by NPM. Profiting from its universal design, the DPIC could be treated as a plug-and-play module for existing detectors, enhancing robustness against adverse weather. To evaluate the reliability of aerial detectors under intricate cloud corruptions, we present an elaborate Hazy-DIOR dataset, which contains numerous images with different cloud conditions and severity levels. Finally, extensive experiments on the Hazy-DIOR and DOTA-Cloud datasets simultaneously demonstrate the robustness of NIRNet, which especially achieves state-of-the-art accuracy and gets 2.16% mean average precision (mAP) and 2.56% relative performance under corruption (rPC) improvements on the Hazy-DIOR compared with solid Oriented R-CNN detector.