TL;DR
Experiments demonstrate that the proposed self-supervised machine vision framework effectively segments camouflaged traces in low-quality, unlabeled outdoor images captured by a mobile robot, outperforming existing COD models in adaptation speed and segmentation accuracy on a challenging custom rescue dataset.
Evaluating multi-source training strategies at the data, pixel, object, and training levels demonstrates that synthetic data effectiveness is not inherent but conditional on how it is generated, structured, and integrated relative to the target architecture and available real data, providing practical guidance for scaling UOD in marine monitoring applications.
AD-YOLO is presented, a dual-level framework that tackles pseudo-label noise and limited multi-scale adaptability when applied to semi-supervised object detection frameworks from both the detector architecture and the SSOD pipeline.
Tracking wildfires in real time is beneficial for fire management response times, studying the environmental impacts of wildfires, and improving single- or multi-sensor autonomy, among many other benefits. Supervised machine learning algorithms enable deep learning models to learn and automatically detect patterns in images efficiently and adaptively. Advances in hardware and associated on-device capabilities are at a stage that makes onboard detection for satellite- and airborne instruments possible. However, supervised methods typically require large hand-labeled datasets. To address this, we train the onboard-capable version of the YOLOv11 instance segmentation architecture with airborne infrared imagery of wildfires using training labels obtained from a separate self-supervised learning framework. Using this newly trained, onboard-capable model, we successfully detect fire sources with a structural similarity index of 0.911 and an intersection over union of 0.796 relative to labels generated by the self-supervised model. The results give 0.850 precision and 0.928 recall in the test dataset, demonstrating the feasibility of self-supervised machine learning for creating training labels. The performance of the self-supervised-to-supervised transfer is evaluated on an emulator of an onboard processor, which found the inference speed to range from 18.6 ms to 50.7 ms with a median of 30.9 ms per 160 by 160 pixel input image tile.
Multimodal fusion methods have shown great potential in remote sensing image analysis, but existing approaches rely heavily on massive amounts of annotated data. This is not only costly and time-consuming but also prone to subjective bias. To address this issue, we propose a category-prior-based self-supervised framework, CGSNet, which uses category prior maps extracted from multispectral images as supervisory signals for end-to-end training. An adaptive confidence-weighted pseudo-label generation mechanism is designed to alleviate noise and errors in prior maps by replacing binary labels with continuous confidence maps, enabling the learning of uncertain interclass features. In addition, a multispectral feature-guided refinement strategy utilizes color and texture information to calibrate class transition regions and enhance the discriminative power of pseudo-labels in complex scenes. A dynamic mask selection strategy further enhances the model’s robustness and generalization capabilities through progressive learning. Experiments demonstrate that CGSNet achieves state-of-the-art performance without the need for human annotation, achieving an Mean Intersection over Union (mIoU) score of 78.46% on the Gaofen image dataset (GID) (vegetation) dataset and 79.58% on the Zurich (vegetation) dataset—12.22% and 15.07% higher than existing methods, respectively—while exhibiting strong cross-dataset zero-shot generalization capabilities. The code will be available at https://github.com/NUAALISILab
Comprehensive comparisons with CNN-, Transformer- and prevailing Mamba-based approaches verify the advantages of SCI-Mamba in visual authenticity, color fidelity and inference speed, and provides a practical low-light enhancement solution for close-proximity non-cooperative space operations.
