Arbitrary-Oriented Object Detection (AOOD) has found broad applications in embodied intelligence, autonomous driving, and satellite remote sensing. However, current AOOD frameworks face challenges in ineffective feature extraction and orientation regression inaccuracy. Inspired by Hilbert curve's intrinsic locality-preserving property, we propose a flexible Hilbert curve-Encoded Rotation-Equivariant Oriented Object Detector (HERO-Det). Our innovations include: (i) a novel Hilbert curve traversal convolution paradigm with a dimensionality reduction scheme, which employs locality-preserving spatial filling curves for feature transformation, (ii) a Hilbert pyramid transformer enabling hierarchical construction of multi-scale feature sequences through space-folding operations, as well as (iii) an orientation-adaptive prediction head that decouples rotation-equivariant regression features from invariant classification cues to resolve orientation regression dilemmas in two-stage detectors. Extensive experiments show HERO-Det achieves state-of-the-art performance on AOOD benchmarks, with mAP of 79.56%, 90.64%, 90.10%, and 80.47% on DOTA, HRSC2016, SSDD, and HRSID, respectively. Performance gains in cross-task validation further demonstrate the versatility of our method to diverse vision tasks, such as medical image segmentation and 3D object detection.

We present MoP-UAV, a new benchmark for UAV-based cross-view object geo-localization guided by multi-modal prompts. MoP-UAV supports fine-grained object-level cross-view localization under diverse prompt modalities, including natural language, bounding boxes, and click points. It offers potential for incorporating large foundation models like large language models (LLMs) and promotes the building of more flexible and intelligent UAV agents. Based on the benchmark, we propose MoPT, a multi-modal-prompt-guided tansformer that embeds prompts as token sequences and extract object location from UAV and satellite features via cross-attention. To enhance semantic consistency and performance, we further adopt a cross-view contrastive loss and propose a RefCOCOg-based pre-training strategy. Extensive experiments show that MoPT achieves robust localization under arbitrary prompt combinations. Notably, multi-modal-prompt training significantly boosts unimodal-prompt inference performance, highlighting the generalization benefits of multi-modal learning. MoPT trained with multi-modal prompts outperforms prior unimodal prompt works under the same setting.

Modern multicore processors increasingly rely on network-on-chip (NoC) architectures to support high-bandwidth and low-latency communication. Traditional mesh-based NoC topologies suffer from uneven load distribution, with central router nodes frequently becoming congestion hot spots. The existing approaches mainly focus on introducing bypass links or independent ring interconnects. However, bypass links can only alleviate congestion between fixed routers, and routers not included in the ring still rely on conventional mesh forwarding, limiting the overall performance improvement. To address these challenges, this article proposes a reinforcement learning-based routing for ring-augmented mesh NoC (RLRM) to improve communication efficiency in multicore processors. First, we design a ring-augmented$8\times 8$mesh topology that integrates horizontal and vertical ring interconnects to achieve balanced load distribution and mitigate congestion hotspots. Second, we equip each router with a topology-aware reinforcement learning (RL) agent that dynamically selects paths based on congestion conditions, thereby proactively avoiding performance bottlenecks. Extensive experiments demonstrate that the proposed solution provides a scalable and efficient architecture for high-performance computing and multicore NoC systems. Experimental results under realistic benchmark traffic patterns show that RLRM reduces average latency by 22.78% compared to conventional mesh and static hybrid routing schemes.

The joint application of multisource remote sensing (MSRS) data, such as hyperspectral image (HSI) and light detection and ranging (LiDAR), offers significant potential for accurate land cover classification. However, the existing applications often struggle with domain shifts across scenes caused by sensor, illumination, and phase variations. Focusing on this domain adaptation problem, a Chirplet Fourier analysis network (ChirpFAN) is proposed for cross-scene classification of MSRS data in this paper. Firstly, a fractional spatial-frequency-phase feature extraction module including the fractional Fourier transform and a learnable phase-aware weighting block is proposed to capture multi-domain features. Secondly, a Chirplet swin transformer (ChirpST) block integrates a Chirplet Fourier analysis (ChirpFA) layer within a Swin transformer is designed to analyze multi-scale textural and oscillatory patterns. Finally, a modality-shared network including ChirpST blocks is designed for inter-modal fusion and alignment. Extensive experiments demonstrate that the ChirpFAN framework achieves state-of-the-art performance with 3% average improvements on three challenging cross-scene MSRS datasets. Code will be released on GitHub.

Medical image segmentation plays an important role in medical diagnosis, and has received extensive attention in recent years. A large number of convolutional neural network based methods have been proposed to achieve accurate segmentation results. Dice loss is the most popular loss function for medical image segmentation tasks. However, we found that Dice loss suffers from abnormal gradient changes, which causes the loss function to be unstable and difficult to converge. Therefore, we propose an gradient-optimized Dice loss (GODC) to solve this problem. GODC corrects the abnormal gradient changes in the segmentation loss, which accelerates the model convergence and can achieve better segmentation performance. Next, we propose a lateral feature alignment module (LFAM). LFAM adopts deformable convolutional network to align the features of different layers on the shortcut connections of U-Net to improve the segmentation performance. Finally, our method achieves state-of-the-art results on the LiTS dataset as well as our collected pancreatic tumor datasets.

Oriented object detection has a very wide range of application scenarios. In recent years, a lot of rotation detectors have been designed to achieve high-performance oriented object detection. Intersection-over-Union (IoU) is the commonly used indicator to evaluate the accuracy of detection performance. Many methods introduce IoU into the bounding box regression loss to achieve the aligned training and evaluation process for better performance. However, in this paper, we demonstrate several drawbacks of rotated IoU loss through both experiments and theoretical derivation: 1) There is a negative correlation between the loss gradient and the angular error. 2) The optimization process of rotated IoU loss suffers from scale sensitivity, which is not conducive to the model convergence. To solve the problems, we propose a Gradient Calibration Loss (GCL) that optimizes the rotated IoU loss via gradient analysis and correction. We construct the optimized gradient in GCL to avoid IoU loss oscillation and scale sensitivity, thereby accelerating model convergence. Models supervised by GCL have a more stable training process, faster convergence, and better performance. Moreover, GCL can be easily introduced into the existing rotation detectors to achieve performance gains without extra inference overhead. Extensive experiments on multiple oriented object detection datasets and models demonstrate the superiority of our method. Our method achieves state-of-the-art performance on the mainstream benchmark datasets. The source code and models are available at https://github.com/ming71/GCL.

Detecting oriented objects in optical remote sensing images has been consistently challenging due to difficulties in bounding boxes localization. The cascaded regression framework, widely employed for high-quality bounding box refinement, has demonstrated effectiveness in this domain. However, our experiments reveal a discontinuity issue in bounding box optimization in cascaded regression framework. As a result, performance gain is not guaranteed across all stages in this framework. In this paper, we propose a Distribution Discriminative Detector(DDDet) to address the above issues and enhance the optimization of bounding boxes in oriented object detection. Specifically, a novel Conditional Anchor Refinement Framework(CARF) is designed to improve cascaded regression structure. CARF distinguishes bounding boxes with different distributions, adaptively optimizing them within the well-assigned regressors. Subsequently, the Aligned Convolution Module(ACM) is integrated into each regressor, facilitating the continuous alignment between features and refined anchors. Furthermore, the Geometry-guided Training Sample Selection(GTSS) method is incorporated into CARF to assign labels based on object shape priors. Experimental results show that DDDet obtains state-of-the-art performance on mainstream datasets for oriented object detection in remote sensing image, which demonstrates the effectiveness of the proposed method. Our method surpasses many current single-stage detectors, two-stage detectors, and refine-stage detectors, achieving the mAP of 79.41% on DOTA dataset, and 44.15% on FAIR1M dataset.

Intersection-over-Union (IoU) is the most popular metric to evaluate regression performance in 3D object detection. Recently, there are also some methods applying IoU to the optimization of 3D bounding box regression. However, we demonstrate through experiments and mathematical proof that the 3D IoU loss suffers from abnormal gradient w.r.t. angular error and object scale, which further leads to slow convergence and suboptimal regression process, respectively. In this paper, we propose a Gradient-Corrected IoU (GCIoU) loss to achieve fast and accurate 3D bounding box regression. Specifically, a gradient correction strategy is designed to endow 3D IoU loss with a reasonable gradient. It ensures that the model converges quickly in the early stage of training, and helps to achieve fine-grained refinement of bounding boxes in the later stage. To solve suboptimal regression of 3D IoU loss for objects at different scales, we introduce a gradient rescaling strategy to adaptively optimize the step size. Finally, we integrate GCIoU Loss into multiple models to achieve stable performance gains and faster model convergence. Experiments on KITTI dataset demonstrate superiority of the proposed method. The code is available at https://github.com/ming71/GCIoU-loss.

How to represent the object more appropriately in oriented object detection is an essential problem to be solved, there are many solutions for the object represented. It is a relatively novel approach to represent objects as a number of sample points useful for both localization and recognition. However, the current point-set-based representation methods do not effectively supervise all points for learning, and the internal information of the convex hull in the point set cannot be effectively learned. Therefore, this letter proposes point set distance (PSD) loss, which learns set-to-set supervision of objects to effectively represent objects. Besides, most of the current sample selection strategies are based on the Intersection over Union (IoU), but these methods cannot comprehensively measure candidate samples quality. To select high-quality point sets, we propose to use the probability distribution of point sets to select the positive samples. Our probabilistic point set sample selection (PPSS) scheme effectively exploits the classification information, regression information, and the distribution characteristics of the point set. Experimental results on remote sensing image datasets including DOTA, DIOR-R, and HRSC2016, demonstrate the proposed method for arbitrary-oriented object detection achieves consistent and substantial improvements.

Currently, reliable and accurate oriented detection in remote sensing images still needs to be improved. The wide variation of object shapes and orientations in the remote sensing images usually leads to two issues in two-stage oriented object detectors. One issue is how to generate high-quality rotation proposals. The other is the angular error sensitivity to the aspect ratios in the angle optimization process. In this paper, we propose a novel rotation proposal generation and optimization detector, which is based on high-quality rotation proposal generation and adaptive angle optimization to solve these two issues. The proposed method mainly establishes the geometric relationship guided region proposal networks (GRG-RPN) and the adaptive angle optimization head (AAO-Head) to achieve more accurate oriented object detection. The GRG-RPN only uses a simple network and a small number of horizontal anchors to predict high-quality rotation proposals. This approach was derived via the calculation based on the theoretical analysis of the geometric relationship between the oriented bounding boxes (OBBs) and their external horizontal bounding boxes (EHBBs). The AAO-Head solves the angular error sensitivity to the aspect ratios and achieves adaptive angle optimization using a new regression parameter, which is defined based on the theoretical analysis of the relationship between the Intersection over Union (IoU), the angular errors and the aspect ratios. Experiments show that our method can achieve 2.5% mAP improvement averagely versus the compared SOTA methods, and achieve 0.5% mAP improvement versus the next best method Oriented R-CNN with fewer regression parameters and a simpler regression approach.

Arbitrary-oriented objects exist widely in remote sensing images. The mainstream rotation detectors use oriented bounding boxes (OBBs) or quadrilateral bounding boxes (QBBs) to represent the rotating objects. However, these methods suffer from the representation ambiguity for oriented object definition, which leads to suboptimal regression optimization and the inconsistency between the loss metric and the localization accuracy of the predictions. In this letter, we propose a representation invariance loss (RIL) to optimize the bounding box regression for the rotating objects in the remote sensing images. RIL treats multiple representations of an oriented object as multiple equivalent local minima and hence transforms bounding box regression into an adaptive matching process with these local minima. Next, the Hungarian matching algorithm is adopted to obtain the optimal regression strategy. Besides, we propose a normalized rotation loss to alleviate the weak correlation between different variables and their unbalanced loss contribution in OBB representation. Extensive experiments on remote sensing datasets show that our method achieves consistent and substantial improvement. The code and models are available athttps://github.com/ming71/RIDetto facilitate future research.

Object detection in optical remote-sensing images is an important and challenging task. In recent years, the methods based on convolutional neural networks (CNNs) have made good progress. However, due to the large variation in object scale, aspect ratio, as well as the arbitrary orientation, the detection performance is difficult to be further improved. In this article, we discuss the role of discriminative features in object detection, and then propose a critical feature capturing network (CFC-Net) to improve detection accuracy from three aspects: building powerful feature representation, refining preset anchors, and optimizing label assignment. Specifically, we first decouple the classification and regression features, and then construct robust critical features adapted to the respective tasks of classification and regression through the polarization attention module (PAM). With the extracted discriminative regression features, the rotation anchor refinement module (R-ARM) performs localization refinement on preset horizontal anchors to obtain superior rotation anchors. Next, the dynamic anchor learning (DAL) strategy is given to adaptively select high-quality anchors based on their ability to capture critical features. The proposed framework creates more powerful semantic representations for objects in remote-sensing images and achieves high-performance real-time object detection. Experimental results on three remote-sensing datasets including HRSC2016, DOTA, and UCAS-AOD show that our method achieves superior detection performance compared with many state-of-the-art approaches. Code and models are available athttps://github.com/ming71/CFC-Net.

Arbitrary-oriented objects widely appear in natural scenes, aerial photographs, remote sensing images, etc., and thus arbitrary-oriented object detection has received considerable attention. Many current rotation detectors use plenty of anchors with different orientations to achieve spatial alignment with ground truth boxes. Intersection-over-Union (IoU) is then applied to sample the positive and negative candidates for training. However, we observe that the selected positive anchors cannot always ensure accurate detections after regression, while some negative samples can achieve accurate localization. It indicates that the quality assessment of anchors through IoU is not appropriate, and this further leads to inconsistency between classification confidence and localization accuracy. In this paper, we propose a dynamic anchor learning (DAL) method, which utilizes the newly defined matching degree to comprehensively evaluate the localization potential of the anchors and carries out a more efficient label assignment process. In this way, the detector can dynamically select high-quality anchors to achieve accurate object detection, and the divergence between classification and regression will be alleviated. With the newly introduced DAL, we can achieve superior detection performance for arbitrary-oriented objects with only a few horizontal preset anchors. Experimental results on three remote sensing datasets HRSC2016, DOTA, UCAS-AOD as well as a scene text dataset ICDAR 2015 show that our method achieves substantial improvement compared with the baseline model. Besides, our approach is also universal for object detection using horizontal bound box. The code and models are available at https://github.com/ming71/DAL.

Boundary discontinuity and its inconsistency to the final detection metric have been the bottleneck for rotating detection regression loss design. In this paper, we propose a novel regression loss based on Gaussian Wasserstein distance as a fundamental approach to solve the problem. Specifically, the rotated bounding box is converted to a 2-D Gaussian distribution, which enables to approximate the indifferentiable rotational IoU induced loss by the Gaussian Wasserstein distance (GWD) which can be learned efficiently by gradient back-propagation. GWD can still be informative for learning even there is no overlapping between two rotating bounding boxes which is often the case for small object detection. Thanks to its three unique properties, GWD can also elegantly solve the boundary discontinuity and square-like problem regardless how the bounding box is defined. Experiments on five datasets using different detectors show the effectiveness of our approach, and codes are available at https://github.com/yangxue0827/RotationDetection.

Existing rotated object detectors are mostly inherited from the horizontal detection paradigm, as the latter has evolved into a well-developed area. However, these detectors are difficult to perform prominently in high-precision detection due to the limitation of current regression loss design, especially for objects with large aspect ratios. Taking the perspective that horizontal detection is a special case for rotated object detection, in this paper, we are motivated to change the design of rotation regression loss from induction paradigm to deduction methodology, in terms of the relation between rotation and horizontal detection. We show that one essential challenge is how to modulate the coupled parameters in the rotation regression loss, as such the estimated parameters can influence to each other during the dynamic joint optimization, in an adaptive and synergetic way. Specifically, we first convert the rotated bounding box into a 2-D Gaussian distribution, and then calculate the Kullback-Leibler Divergence (KLD) between the Gaussian distributions as the regression loss. By analyzing the gradient of each parameter, we show that KLD (and its derivatives) can dynamically adjust the parameter gradients according to the characteristics of the object. For instance, it will adjust the importance (gradient weight) of the angle parameter according to the aspect ratio. This mechanism can be vital for high-precision detection as a slight angle error would cause a serious accuracy drop for large aspect ratios objects. More importantly, we have proved that KLD is scale invariant. We further show that the KLD loss can be degenerated into the popular Ln-norm loss for horizontal detection. Experimental results on seven datasets using different detectors show its consistent superiority, and codes are available at https://github.com/yangxue0827/RotationDetection.

SWIM is based on the backbone of network and key information technology, with combination of multiple information management projects, provide information, exchange technical support and cross sharing data for different unit and departments, different information platforms and systems. SWIM improves the network structure, meanwhile makes the IT infrastructure construction and business application function development to maintain a certain degree of loose coupling, ensure interoperability between two systems, and making full use of the old ATM system Infrastructure, allowing users to focus more on business application development, better meet their operational needs, improve application development speed and flexibility, reduce the cost of civil aviation business system construction. Analytical Hierarchy Process (AHP) in fuzzy mathematics is used to solve multi-objective, multi-criteria or complex structure of large-scale decision-making problem, providing a scientific and effective way to decompose large-scale system like SWIM.