Adverse conditions such as intense illumination and inclement weather pose challenges to object detection tasks. Current methodologies aim to enhance detector performance by improving image quality through various means. In low-visibility agricultural scene object detection, improving detection accuracy is insufficient with image quality enhancement alone. Existing detectors designed for low-visibility conditions do not exhibit strong performance in agricultural scenes. In this paper, we introduce a novel benchmark called Low-visibility Crop Detection (LVCD), extending the low-visibility task to agricultural scenes. Furthermore, we propose a foundational framework, Point cloud Guided Segmentation Network (PGSNet), which learns additional point cloud cues to compensate for the missing details of targets caused by low-visibility in images, extending the model’s representational capacity to two modalities. To facilitate research, we gather a multi-modal dataset, LVScene4K, comprising images and corresponding point clouds of various crop types. In order to address the challenges posed by low-visibility, we specifically designed corresponding modules. The encoder component encodes features at multiple scales and constructs multi-scale receptive fields to enable the model to simultaneously extract more detailed features from both images and point clouds. The decoder analyzes both local and global details as well as texture features of the target, while adaptively capturing characteristics under various adverse conditions to improve the model’s discriminative performance under low-visibility conditions. In the end, the model applies iterative refinement strategy for progressively optimizing the detection outcomes. Comprehensive experiments conducted on the LVScene4K demonstrate the effectiveness and robustness of PGSNet in the LVCD task.

Synthetic aperture radar (SAR) target recognition frequently encounters the emergence of novel targets. Updating and iterating existing models with newly arriving data constitutes an incremental learning task. However, training with only new data often causes catastrophic forgetting, leading to a substantial loss of performance on previously learned classes, while retraining the entire model is prohibitively costly. This necessitates the development of algorithms with strong continual learning capabilities. Moreover, compared with traditional optical scenarios, SAR incremental tasks pose even greater challenges: due to the imaging mechanism, target characteristics, and application scenarios, they are highly sensitive to azimuth variations, exhibit low inter-class variability, and demand lightweight solutions. To address these issues, this paper proposes a scattering-aware adaptive dynamic node generation (SDG) algorithm for class-incremental learning. First, the attributed scattering center model (ASCM) is integrated to extract representative prototype vectors, which enhance spatial attention and calibrate the parameters of the expanded backbone network. Second, a gradient-constrained and indicator-driven dynamic network expansion strategy, combined with parameter minimization, ensures both model compactness and efficiency. Finally, a multi-teacher knowledge distillation approach is introduced, where different backbones serve as distinct teachers to mitigate the low inter-class variability inherent in SAR images. Extensive experiments on the MSTAR, SAR-AIRcraft, and FUSAR-Ship datasets demonstrate that SDG achieves significant improvements in both recognition accuracy and parameter efficiency. This study thus provides a lightweight and effective solution for real-time SAR target recognition on resource-constrained platforms.

Concealed Object Detection (COD) aims to identify objects visually embedded in their background. Existing COD datasets and methods predominantly focus on animals or humans, ignoring the agricultural domain, which often contains numerous, small, and concealed crops with severe occlusions. In this paper, we introduce Concealed Crop Detection (CCD), which extends classic COD to agricultural domains. Experimental study shows that unimodal data provides insufficient information for CCD. To address this gap, we first collect a large-scale RGB-D dataset, ACOD-12K, containing high-resolution crop images and depth maps. Then, we propose a foundational framework named Recurrent Iterative Segmentation Network (RISNet). To tackle the challenge of dense objects, we employ multi-scale receptive fields to capture objects of varying sizes, thus enhancing the detection performance for dense objects. By fusing depth features, our method can acquire spatial information about concealed objects to mitigate disturbances caused by intricate backgrounds and occlusions. Furthermore, our model adopts a multi-stage iterative approach, using predictions from each stage as gate attention to reinforce position information, thereby improving the detection accuracy for small objects. Extensive experimental results demonstrate that our RISNet achieves new state-of-the-art performance on both newly proposed CCD and classic COD tasks. All resources will be available at https://github.com/Kki2Eve/RISNet.

The reliable recovery and uncertainty quantification of a fixed effect function $\mu$ in a functional mixed model, for modeling population- and object-level variability in noisily observed functional data, is a notoriously challenging task: variations along the $x$ and $y$ axes are confounded with additive measurement error, and cannot in general be disentangled. The question then as to what properties of $\mu$ may be reliably recovered becomes important. We demonstrate that it is possible to recover the size-and-shape of a square-integrable $\mu$ under a Bayesian functional mixed model. The size-and-shape of $\mu$ is a geometric property invariant to a family of space-time unitary transformations, viewed as rotations of the Hilbert space, that jointly transform the $x$ and $y$ axes. A random object-level unitary transformation then captures size-and-shape preserving deviations of $\mu$ from an individual function, while a random linear term and measurement error capture size-and-shape altering deviations. The model is regularized by appropriate priors on the unitary transformations, posterior summaries of which may then be suitably interpreted as optimal data-driven rotations of a fixed orthonormal basis for the Hilbert space. Our numerical experiments demonstrate utility of the proposed model, and superiority over the current state-of-the-art.

Camouflaged Object Detection (COD) aims to identify and segment objects that blend into their surroundings. Since the color and texture of the camouflaged objects are extremely similar to the surrounding environment, it is super challenging for vision models to precisely detect them. Inspired by research on biology and evolution, we introduce depth information as an additional cue to help break camouflage, which can provide spatial information and texture-free separation for foreground and background. To dig clues of camouflaged objects in both RGB and depth modalities, we innovatively propose Depth-aided Camouflaged Object Detection (DaCOD), which involves two key components. We firstly propose the Multi-modal Collaborative Learning (MCL) module, which aims to collaboratively learning deep features from both RGB and depth channels via a hybrid backbone. Then, we propose a novel Cross-modal Asymmetric Fusion (CAF) strategy, which asymmetrically fuse RGB and depth information for complementary depth feature enhancement to produce accurate predictions. We conducted numerous experiments of the proposed DaCOD on three widely-used challenging COD benchmark datasets, in which DaCOD outperforms the current state-of-the-arts by a large margin. All resources are available at https://github.com/qingwei-wang/DaCOD.