Semi-Supervised Object Detection (SSOD) aims to improve the utilization of unlabeled data, and various methods, such as adaptive threshold techniques, have been extensively studied to increase exploitable information. However, these methods are passive, relying solely on the original image data. Additionally, existing approaches prioritize the predicted categories of the teacher model while overlooking the relationships between different categories in the prediction. In this paper, we introduce a novel approach called Dense Information Learning (DIL), which actively generates unlabeled data containing densely exploitable information and forces the network to have relation consistency under different perturbations. Specifically, Dense Information Augmentation (DIA) leverages the prior information of the network to create a foreground bank and actively incorporates exploitable information into the unlabeled data. DIA automatically performs information enhancement and filters noise. Furthermore, to encourage the network to maintain consistency at the manifold level under various perturbations, we introduce Relation Consistency Regularization (RCR). It considers both feature-level and image-level perturbations, guiding the network to focus on more discriminative features. Extensive experiments conducted on multiple datasets validate the effectiveness of our approach in leveraging information from unlabeled images. The proposed DIL improves the mAP by 12.6% and 10.0% relative to the supervised baseline method when utilizing 5% and 10% of labeled data on the MS-COCO dataset, respectively.

Semi-supervised object detection (SSOD) aims to solve the data annotation challenge in object detection and can achieve remarkable progress in natural scenes; however, it remains unexplored in horizontal bounding box (HBB)-based remote sensing imagery where annotation tasks pose greater challenges. In remote sensing scenarios, objects exhibit arbitrary orientations, small scales, and dense distributions, leading to pseudoboxes with fuzzy boundaries and class imbalance issues. Therefore, we propose UNCertainty quantification (UNC) for SSOD in remote sensing images. UNC uses uncertainty to guide the network from both regression and classification perspectives: Semantic alignment SAM calibration (SASC) uses pseudoboxes as box prompts for the input of the segment anything model (SAM), achieving more precise boundaries. Subsequently, boundaries with lower regression uncertainty are selected as the final pseudoboxes, ensuring better alignment between the pseudoboxes and the ground truth. Dynamic uncertainty weighting (DUW) calculates class uncertainty and determines its correlation with the availability of instances per class. High uncertainty implies limited availability of instances, necessitating greater emphasis on instances of that class. Furthermore, we set a percentage uncertainty threshold to avoid overemphasis caused by individual classes. Extensive experiments conducted on the DIOR and DOTA HBB-based datasets demonstrate the effectiveness of our method in leveraging unlabeled image information. Specifically, compared with the supervised baseline method, the UNC method improves mAP by 12.4% and 8.6% when 5% and 10% of labeled data on DIOR, respectively.

Semi-Supervised Object Detection (SSOD) has shown remarkable results by leveraging image pairs with a teacher-student framework. An excellent strong augmentation method can generate richer images and alleviate the influence of noise in pseudo-labels. However, existing data augmentation methods for SSOD do not consider instance-level information, thus, they cannot make full use of unlabeled data. Besides, the current teacher-student framework in SSOD solely relies on pseudo-labeling techniques, which may disregard some uncertain information. In this article, we introduce a new method called Elaborate Teacher which generates and exploits image pairs in a more refined manner. To enrich strongly augmented images, a novel data augmentation method called Information-Aware Mixup Representation (IAMR) is proposed. IAMR utilizes the teacher model's predictions as prior information and considers instance-level information, which can be seamlessly integrated with existing SSOD data augmentation methods. Furthermore, to fully exploit the information in unlabeled data, we propose the Enhanced Scale Consistency Regularization (ESCR), which considers the consistency from both semantic space and feature space. Elaborate Teacher introduces a fresh data augmentation method, complemented by consistency regularization, which boosts the performance of semi-supervised object detectors. Extensive experiments on thePASCAL VOCandMS-COCOdatasets demonstrate the effectiveness of our method in leveraging unlabeled image information. Our method consistently outperforms the baseline method and improves mAP by 11.6% and 9.0% relative to the supervised baseline method when using 5% and 10% of labeled data onMS-COCO, respectively.