A weakly-supervised oriented object detector : Knowledge-based dropblock and unified regression network

Object detection in remote sensing images (RSIs) is facilitated by oriented bounding boxes, yet rotated boxes (RBoxes) are typically more labor-intensive than horizontal boxes (HBoxes). Consequently, there is a tendency in most research to explore HBox-based weakly-supervised detectors with self-supervised constraints on spatial transformations. However, such weakly-supervised networks for HBoxes tend to focus on the most discriminative parts of objects, which can adversely affect the network's location accuracy. Moreover, spatial transformations introduce an ambiguity between RBoxes and HBoxes in the regression loss, detrimentally affecting the network's ability to accurately distinguish closely situated objects at the same angle. To overcome these challenges, we propose a weakly-supervised detector named knowledge-based dropblock and unified regression network (KDUNet). This network aims to learn high-quality feature information and compensate for the disparity between HBoxes and RBoxes. Initially, we use long-distance background information with diverse channel input to intentionally conceal the most distinguishable parts, thus emphasizing the entire object. Furthermore, we have developed a clear bounding box distance measure that unifies RBoxes and HBoxes through a circumscribed rectangle with a transformation angle to assess their Gaussian distance. Extensive experiments demonstrate that KDUNet is capable of learning high-quality feature information and reducing the impact of ambiguity. Experimental results on the DIOR and HRSC datasets confirm that our network surpasses six fully-supervised networks, achieving 57.8 % and 90.1 % mean Average Precision (mAP) respectively.