Background noise contamination in encoder features and insufficient directional modeling on the decoder side remain central challenges in arbitrary-oriented object detection (AOOD). To address these issues, an encode–transmit–decode detector (ETD-Det) is presented, co-optimized across encoding, transmission, and decoding. On the encoding side, a spectral diffusion encoder (LapSpecEnc) is proposed. Frequency-domain diffusion is performed by LapSpecEnc to suppress high-frequency background noise and to maintain coherent, orientation-consistent features. On the decoding side, an Extended Gaussian Distribution (EGD) together with a Kullback–Leibler (KL) divergence-based loss (EGKLD) are introduced. This design provides direction-sensitive geometric representation and mitigates the angle-gradient degradation observed in standard Gaussian formulations, particularly for nearly square targets. Extensive evaluation has been conducted, demonstrating strong effectiveness and generalization: ETD-Det achieves 90.61% mAP on HRSC2016, 90.37% mAP on UCAS-AOD, 81.54% mAP on DOTA-v1.0, and an F-measure of 82.33% on ICDAR2015. These results surpass existing methods under comparable settings and support ETD-Det as a robust and accurate solution for high-precision AOOD.

Conventional object detection methods face challenges in addressing the complexity of targets in optical remote sensing images (ORSIs), including multi-scale objects, high aspect ratios, and arbitrary orientations. This study proposes a novel detection framework called Progressive Self-Modulating Detector (PSMDet), which incorporates self-modulation mechanisms at the backbone, feature pyramid network (FPN), and detection head stages to address these issues. The backbone network utilizes a reparameterized large kernel network (RLK-Net) to enhance multi-scale feature extraction. At the same time, the adaptive perception network (APN) achieves accurate feature alignment through a self-attention mechanism. Additionally, a Gaussian-based bounding box representation and smooth relative entropy (${\mathrm{smooth}}_{\mathrm{RE}}$) regression loss are introduced to address traditional bounding box regression challenges, such as discontinuities and inconsistencies. Experimental validation on the HRSC2016 and UCAS-AOD datasets demonstrates the framework’s robust performance, achieving the mean Average Precision (mAP) scores of 90.69% and 89.86%, respectively. Although validated on ORSIs, the proposed framework is adaptable for broader applications, such as autonomous driving in intelligent transportation systems and defect detection in industrial vision, where high-precision object detection is essential. These contributions provide theoretical and technical support for advancing intelligent image sensor-based applications across multiple domains.

Object detection in aerial images has had a broader range of applications in the past few years. Unlike the targets in the images of horizontal shooting, targets in aerial photos generally have arbitrary orientation, multi-scale, and a high aspect ratio. Existing methods often employ a classification backbone network to extract translation-equivariant features (TEFs) and utilize many predefined anchors to handle objects with diverse appearance variations. However, they encounter misalignment at three levels, spatial, feature, and task, during different detection stages. In this study, we propose a model called the Staged Adaptive Alignment Detector (SAADet) to solve these challenges. This method utilizes a Spatial Selection Adaptive Network (SSANet) to achieve spatial alignment of the convolution receptive field to the scale of the object by using a convolution sequence with an increasing dilation rate to capture the spatial context information of different ranges and evaluating this information through model dynamic weighting. After correcting the preset horizontal anchor to an oriented anchor, feature alignment is achieved through the alignment convolution guided by oriented anchor to align the backbone features with the object’s orientation. The decoupling of features using the Active Rotating Filter is performed to mitigate inconsistencies due to the sharing of backbone features in regression and classification tasks to accomplish task alignment. The experimental results show that SAADet achieves equilibrium in speed and accuracy on two aerial image datasets, HRSC2016 and UCAS-AOD.

Oriented objects in RSI (Remote Sensing Imagery) typically present arbitrary rotations, extreme aspect ratios, multi-scale variations, and complex backgrounds. These factors often result in feature misalignment, representational ambiguity, and regression inconsistency, which significantly degrade detection performance. To address these issues, GAANet (Gaussian-Augmented Additive Network), a symmetry-driven framework for ODD (oriented object detection), is proposed. GAANet incorporates a symmetry-preserving mechanism into three critical components—feature extraction, representation modeling, and metric optimization—facilitating systematic improvements from structural representation to learning objectives. A CAX-ViT (Contextual Additive Exchange Vision Transformer) is developed to enhance multi-scale structural modeling by combining spatial–channel symmetric interactions with convolution–attention fusion. A GBBox (Gaussian Bounding Box) representation is employed, which implicitly encodes directional information through the invariance of the covariance matrix, thereby alleviating angular periodicity problems. Additionally, a GPIoU (Gaussian Product Intersection over Union) loss function is introduced to ensure geometric consistency between training objectives and the SkewIoU evaluation metric. GAANet achieved a 90.58% mAP on HRSC2016, 89.95% on UCAS-AOD, and 77.86% on the large-scale DOTA v1.0 dataset, outperforming mainstream methods across various benchmarks. In particular, GAANet showed a +3.27% mAP improvement over ${\mathrm{R}}^3$Det and a +4.68% gain over Oriented R-CNN on HRSC2016, demonstrating superior performance over representative baselines. Overall, GAANet establishes a closed-loop detection paradigm that integrates feature interaction, probabilistic modeling, and metric optimization under symmetry priors, offering both theoretical rigor and practical efficacy.

Chromosomes are essential carriers of human genetic material, and karyotype diagnosis plays a crucial role in prenatal diagnostics, genetic disease identification, and medical research. Physicians rely heavily on karyotype images to diagnose potential abnormalities in chromosome numbers and structure. However, the process is tedious and challenging. To improve diagnostic efficiency and accuracy, artificial intelligence (AI) researchers have developed convolutional neural networks (CNNs) for chromosome image classification. Despite this progress, the gap between cytogeneticists and AI experts results in a time-consuming workflow. In this study, we propose a framework based on improved Differentiable Architecture Search (DARTS) to automatically design convolutional architectures for the classification task. The improvement strategies based on DARTS are implemented in two stages. First, a procedural approach was designed to comprehensively analyze the evolution of architectural parameters. Based on this analysis, the search space of the DARTS algorithm was refined, resulting in an optimized search space. Next, an entropy-based regularization term was incorporated into the supernetwork’s objective function to guide the algorithm in searching for a more effective architecture. Then, extensive experiments were conducted on CIFAR-10, ImageNet, and the Copenhagen datasets to evaluate the performance of the searched architecture in comparison with related works. The network composed of the searched architecture achieved accuracies of 97.27 $\pm$ 0.05%, 75.40%, and 98.64% on the three datasets, respectively. These results demonstrate that the architecture is high-performing and the proposed framework for designing networks for chromosome classification is effective.

Object Detection (OD) in Remote Sensing Imagery (RSI) encounters significant challenges such as multi-scale variation, high aspect ratios, and densely distributed objects. These challenges often result in misalignments among Bounding Box (BBox) representation, Label Assignment (LA) strategies, and regression loss functions. To address these limitations, this study proposes a novel detection framework, the Gaussian Detection (GaussianDet) Framework, that integrates probabilistic modeling with dynamic sample assignment to achieve more precise OD. The core design of this framework is inspired by the theory of geometric symmetry. Specifically, the radial symmetry of a two-dimensional Gaussian distribution is employed to capture the rotational and scale-invariant properties of Remote Sensing (RS) objects. By leveraging the axial symmetry of elliptical geometry, the proposed Gaussian Elliptical Intersection over Union (GEIoU) enables rotation-aligned matching, while Omni-dimensional Adaptive Assignment (ODAA) introduces dynamic symmetric constraints to optimize the spatial distribution of training samples. Specifically, a Flexible Bounding Box (FBBox) representation based on a 2D Gaussian distribution is introduced to more accurately characterize the shape, aspect ratio, and orientation of objects. In addition, the GEIoU is designed as a scale-invariant similarity metric to align regression loss with detection accuracy. To further enhance sample quality and feature learning, the ODAA strategy adaptively selects positive samples based on object scale and geometric constraints. Experimental results on the High-Resolution Ship Collection 2016 (HRSC2016) and University of Chinese Academy of Sciences–Aerial Object Detection (UCAS-AOD) datasets demonstrate that GaussianDet achieves mean Average Precision (mAP) scores of 90.53% and 96.24%, respectively. These results significantly outperform existing Oriented Object Detection (OOD) methods, thereby validating the effectiveness of the proposed approach and providing a solid theoretical foundation for future research in Remote Sensing Object Detection (RSOD).