ABSTRACT In recent years, robot path planning has become a critical aspect of autonomous navigation, especially in dynamic and complex environments where robots must operate efficiently and safely. One of the primary challenges in this domain is achieving high convergence efficiency while avoiding local optimal solutions, which can hinder the robot's ability to find the best possible path. Additionally, ensuring that the robot follows a path with minimal turns and reduced path length is essential for enhancing operational efficiency and reducing energy consumption. These challenges become even more pronounced in high‐dimensional optimization tasks where the search space is vast and difficult to navigate. In this article, a multi‐strategy fusion enhanced dung beetle optimization algorithm (MIDBO) is introduced to tackle key challenges in robot path planning, such as slow convergence and the problem of local optima, and so on, in which MIDBO incorporates several key innovations to enhance performance and robustness. First, the Tent chaotic strategy is used to diversify initial solutions during population initialization, thereby mitigating the risk of local optima and improving global search capability. Second, a penalty term is integrated into the fitness function to penalize excessive turning angles, aiming to reduce the frequency and magnitude of turns. This modification results in smoother and more efficient paths with reduced lengths. Third, the inertia weight is adaptively updated by a sine‐based mechanism, which dynamically balances exploration and exploitation, accelerates convergence, and enhances algorithm stability. To further improve efficiency for path planning, the MIDBO integrates a Levy flight strategy and a local search mechanism to boost the search capability during the stealing phase, contributing to smoother and more practical paths planned for the robot. A series of thorough and reproducible experiments are performed using benchmark test functions to evaluate the performance of MIDBO in comparison to several leading metaheuristic algorithms. The results demonstrate that MIDBO achieves superior outcomes in path planning tasks with optimal and mean path lengths of 42.1068 and 44.4755, respectively, which significantly outperforms other algorithms including IPSO (47.6244, 55.9375), original DBO (47.6244, 55.9375), and ISSA (47.6244, 55.9375). MIDBO also markedly reduces the number of turns by achieving best and average values of 10 and 13.4, respectively, compared with IPSO (11, 16.1), original DBO (12, 15.3), and ISSA (12, 16.4). Besides, the consistent performance of MIDBO is confirmed via stability analysis based on the mean square error of path lengths and turn counts across 10 independent trials. For the high‐dimensional optimization tasks, MIDBO achieves 8 and 7 functions about top rankings on 50‐ and 100‐dimensional functions, and specifically MIDBO outperforms DBO, IPSO, and ISSA on 13, 18, and 11 functions, respectively. Therefore, the findings validate MIDBO is a competitive solution of path planning for mobile robot navigation with complex requirements.

Fine-grained object detection (FGOD) in remote sensing images is an emerging and challenging task in the field of image intelligent interpretation. It aims to localize objects while classifying them into different fine-grained categories. Modern FGOD methods are mainly derived from well-developed detectors and have made compelling progress. Despite this, these methods struggle to perform well in classifying objects at the subordinate level due to the limitations of their representation manners. In this paper, we propose a network capable of learning discriminative representation (DR) for fine-grained object detection in remote sensing images, named DRNet. First, a fine-grained branch that works in parallel with other task branches is introduced, where objects’ features are re-encoded with dual refinement to generate discriminative representation, enabling accurate fine-grained classification. Second, we design a confusion-minimized loss that automatically scales loss contributions according to the separability of samples to train the fine-grained branch, further boosting discriminative ability of the representation and better addressing hard-to-distinguish objects. Moreover, we devise an interaction verification strategy that empowers the network to fully utilize the results of fine-grained classification and coarse classification for achieving robust inference. On large-scale FAIR1M-1.0 and FAIR1M-2.0 datasets, our DRNet with ResNet50 and$1\times $training schedule obtains 40.87% mAP and 47.04% mAP, respectively, establishing new state-of-the-arts for fine-grained object detection in remote sensing images. The source code is available athttps://github.com//54wb//DRNet.

In object detection, particularly within remote sensing images, the quality of selected samples is crucial for the accuracy and robustness of detection models. However, current sampling strategies demonstrate inherent limitations. They empirically define positive sample sets using fixed thresholds or preset areas, ignoring the actual shapes of the objects and failing to distinguish the intrinsic value of each sample point. To address these critical issues, this article proposes a novel centric probability-based sample selection approach that includes centering probability mapping (CPM), Expectation-Maximization-based boundary optimization (EBO), and probabilistic random sampling (PRS) technologies. Specifically, the CPM is constructed to assign various confidence levels for all sample points based on their proximity to the center of bounding box, effectively discerning the value of individual samples. Then, the EBO is utilized to dynamically optimize the boundaries for positive and negative samples based on the EM algorithm, thus avoiding the sample imbalance problem associated with empirical thresholds. Finally, the PRS strategy is proposed to select training samples from the sample space constructed by CPM and EBO in a manner of random probability sampling, which could improve the diversity of samples while guaranteeing their quality. Experimental validation on three remote sensing image datasets, including DOTA-v1.0, DOTA-v2.0, and DIOR-R, demonstrates that our method achieves robust performance improvements over baseline and significantly surpasses the advanced sample selection methods. The source code will be available athttps://github.com/yanqingyao1994/CPSS.

Within remote sensing images, complex atmospheric environments commonly bring about distinct variations in imaging visibility and ambient occlusions, significantly transforming the appearance of objects. Nevertheless, modern detectors generally struggle to maintain promising accuracy when encountering realistic scenarios. Devoting to alleviating the issues, we develop a noise incentive robust network (NIRNet) for remote sensing object detection under cloud corruption without relying on hazy images for training. The proposed NIRNet preserves discriminative representations and calibrates them using an incentive mechanism. Firstly, we design a noise perception module (NPM) to deal with diverse cloud corruption types, which generates point-wise calibration weights dependent on the perceived discrepancy between objects and environmental noise. Secondly, aiming to detect difficult-to-discern objects thoroughly, a dual-path incentive calibration (DPIC) strategy is proposed to combine intensity and stability features weighted by NPM. Profiting from its universal design, the DPIC could be treated as a plug-and-play module for existing detectors, enhancing robustness against adverse weather. To evaluate the reliability of aerial detectors under intricate cloud corruptions, we present an elaborate Hazy-DIOR dataset, which contains numerous images with different cloud conditions and severity levels. Finally, extensive experiments on the Hazy-DIOR and DOTA-Cloud datasets simultaneously demonstrate the robustness of NIRNet, which especially achieves state-of-the-art accuracy and gets 2.16% mAP and 2.56% rPC improvements on the Hazy-DIOR compared to solid Oriented R-CNN detector. The code is available at https://github.com/zhangpeng2001/nirnet.

In recent years, few-shot object detection (FSOD) in remote sensing images has attracted increasing attention. Numerous studies address the challenges posed by both intra-class and inter-class variance through strategies such as augmenting sample diversity and incorporating multi-scale features. However, these features still encompass a considerable amount of noise attributes due to the complex characteristic of satellite images, persistently and adversely affecting classification. In contrast, we advocate for the belief that a limited yet refined set of features surpasses a multitude of coarse features. Accordingly, we tackle above issues through the meticulous refinement of representative category features, enhancing performance by eliminating irrelevant attributes that interfere with classification. Specifically, two pivotal modules: retentive compensation module (RCM) and personality filtering module (PFM), are introduced. The former module RCM systematically scrutinizes features proximate to the category center, yielding prototypes that exhibit both intra-class compactness and inter-class distinctiveness. Furthermore, the latter module PFM utilizes previous obtained prototypes to supervise the filtering process, diminishing the intra-class variance by excluding personality features which could impede the classification task. The integration of the above two modules enables a holistic feature representation, capturing inherent similarities within individual classes while accentuating distinctions between classes. Experiments have been conducted on the DIOR and NWPU VHR-10.v2 datasets, and the results demonstrate that our proposed approach exceeds several state-of-the-art methods. Code is available at https://github.com/yomik-js/RP-FSOD.

Object detection in remote sensing images has garnered significant attention due to its wide applications in real-world scenarios. However, most existing oriented object detectors still suffer from complex backgrounds and varying angles, limiting their performance to further improvement. In this paper, we propose a novel oriented detector withHierarchical mask prompting andRobust integrated regression, termed HRDet. Specifically, to cope with the first issue, we construct a hierarchical mask prompting module consisting of a semantic mask prediction branch and hierarchical Softmax technique. The former aims to isolate object instances from cluttered interferences guided by coarse box-wise masks, while the latter propagates differentiated features for adjacent layers using hierarchical attentive weights. To deal with the second issue, we strive for robust integrated regression and formulate an efficient oriented IoU loss, explicitly measuring the discrepancies of three geometric factors in oriented regression, i.e., the central point distance, side length, and angle. This innovative loss intends to overcome the problem that existing IoU-based losses are invariant during the regression of varying angles. We applied these two strategies to a simple one-stage detection pipeline, achieving a new level of trade-off between speed and accuracy. Extensive experiments on four large aerial imagery datasets, DOTA-v1.0, DOTA-v2.0, DIOR-R, and HRSC2016, demonstrate that our HRDet significantly improves the accuracy of the one-stage detector over refine-stage counterparts while maintaining the efficiency advantage. The source code will be available athttps://github.com/yanqingyao1994/HRDet.

Oriented object detection in aerial images has made significant advancements propelled by well-developed detection frameworks and diverse representation approaches to oriented bounding boxes. However, within modern oriented object detectors, the insufficient consideration given to certain factors, like contextual priors in aerial images and the sensitivity of the angle regression, hinder further improvement of detection performance. In this paper, we propose a dual-focused detector (DFDet), which simultaneously focuses on the exploration of contextual knowledge and the mitigation of angle sensitivity. Specifically, DFDet contains two novel designs: a contextual dependence mining network (CDMN) and a penalty-incentive allocation strategy (PIAS). CDMN constructs multiple features containing contexts across various ranges with low computational burden, and aggregates them into a compact yet informative representation that empowers the model for robust inference. PIAS dynamically calibrates the angle regression loss with a scalable penalty term determined by the angle regression sensitivity, incentivizing model to boost regression capacity for large aspect ratio objects challenging to be localized accurately. Extensive experiments on four widely-used benchmarks demonstrate the effectiveness of our approach, and new state-of-the-arts for one-stage object detection in aerial images are established. Without bells and whistles, DFDet with ResNet50 achieves 74.71% mAP running at 23.4 FPS on the most widely-used DOTA-v1.0 dataset. The source code is available at https://github.com/DDGRCF/DFDet.

With the rise of deep convolutional neural networks, object detection has achieved prominent advances in past years. However, such prosperity could not camouflage the unsatisfactory situation of Small Object Detection (SOD), one of the notoriously challenging tasks in computer vision, owing to the poor visual appearance and noisy representation caused by the intrinsic structure of small targets. In addition, large-scale dataset for benchmarking small object detection methods remains a bottleneck. In this paper, we first conduct a thorough review of small object detection. Then, to catalyze the development of SOD, we construct two large-scale Small Object Detection dAtasets (SODA), SODA-D and SODA-A, which focus on the Driving and Aerial scenarios respectively. SODA-D includes 24828 high-quality traffic images and 278433 instances of nine categories. For SODA-A, we harvest 2513 high resolution aerial images and annotate 872069 instances over nine classes. The proposed datasets, as we know, are the first-ever attempt to large-scale benchmarks with a vast collection of exhaustively annotated instances tailored for multi-category SOD. Finally, we evaluate the performance of mainstream methods on SODA. We expect the released benchmarks could facilitate the development of SOD and spawn more breakthroughs in this field.

Object detection is a fundamental yet challenging task in computer vision. Despite the great strides made over recent years, modern detectors may still produce unsatisfactory performance due to certain factors, such as non-universal object features and single regression manner. In this paper, we draw on the idea of mutual-assistance (MA) learning and accordingly propose a robust one-stage detector, referred as MADet, to address these weaknesses. First, the spirit of MA is manifested in the head design of the detector. Decoupled classification and regression features are reintegrated to provide shared offsets, avoiding inconsistency between feature-prediction pairs induced by zero or erroneous offsets. Second, the spirit of MA is captured in the optimization paradigm of the detector. Both anchor-based and anchor-free regression fashions are utilized jointly to boost the capability to retrieve objects with various characteristics, especially for large aspect ratios, occlusion from similar-sized objects, etc. Furthermore, we meticulously devise a quality assessment mechanism to facilitate adaptive sample selection and loss term reweighting. Extensive experiments on standard benchmarks verify the effectiveness of our approach. On MS-COCO, MADet achieves 42.5% AP with vanilla ResNet50 backbone, dramatically surpassing multiple strong baselines and setting a new state of the art.

Fine-grained oriented object recognition (FGO2R) is a practical need for intellectually interpreting remote sensing images. It aims at realizing fine-grained classification and precise localization with oriented bounding boxes, simultaneously. Our considerations for the task are general but decisive: (i) the extraction of subtle differences carries a big weight in differentiating fine-grained classes, and (ii) oriented localization prefers rotation-sensitive features. In this article, we propose a network with separate feature refinement (SFRNet), in which two transformer-based branches are designed to perform function-specific feature refinement for fine-grained classification and oriented localization, separately. To highlight the discriminative information advantageous to fine-grained classification, we propose a spatial and channel transformer (SC-Former) to capture both the long-range spatial interactions and the key correlations hidden in the feature channels. Besides, we design a Multi-RoI loss (MRL) following the protocol of deep metric learning to enhance the separability of fine-grained classes further. For oriented localization, we integrate the oriented response convolution with the transformer structure (namely, OR-Former) to assist in encoding rotation information during regression. Extensive experimental results validate the effectiveness and robustness of our SFRNet. Without bells and whistles, our SFRNet achieves state-of-the-art performance on the large-scale FAIR1M datasets (FAIR1M-1.0 and FAIR1M-2.0). Code will be available at https://github.com/Ranchosky/SFRNet.

Oriented object detectors have suffered severely from the discontinuous boundary problem for a long time. In this work, we ingeniously avoid this problem by relating regression outputs to regression target orientations. The core idea of our method is to build a contour function which imports orientations and outputs the corresponding distance predictions. Inspired by Fourier transformations, we assume this function can be represented as a linear combination of trigonometric functions and Fourier series. We replace the final 4D layer in the regression branch of fully convolutional one-stage object detector (FCOS) with a Fourier Series Transformation (FST) module and term this new network FCOSF. By this unique design, the regression outputs in FCOSF can adaptively vary according to the regression target orientations. Thus, the discontinuous boundary has no impact on our FCOSF. More importantly, FCOSF avoids building complicated oriented box representations, which usually cause extra computations and ambiguities. With only flipping augmentation and single-scale training and testing, FCOSF with ResNet-50 achieves 73.64% mAP on the DOTA-v1.0 dataset with up to 23.6 FPS speed, surpassing all one-stage oriented object detectors. On the more challenging DOTA-v2.0 dataset, FCOSF also achieves the highest results of 51.75% mAP among one-stage detectors. More experiments on DIOR-R and HRSC2016 are also conducted to verify the robustness of FCOSF. Code and models will be available at https://github.com/DDGRCF/FCOSF.

Detecting objects in remote sensing images (RSIs) using oriented bounding boxes (OBBs) is flourishing but challenging, wherein the design of OBB representations is the key to achieving accurate detection. In this article, we focus on two issues that hinder the performance of the two-stage oriented detectors: 1) the notorious boundary discontinuity problem, which would result in significant loss increases in boundary conditions, and 2) the inconsistency in regression schemes between the two stages. We propose a simple and effective bounding box representation by drawing inspiration from the polar coordinate system and integrate it into two detection stages to circumvent the two issues. The first stage specifically initializes four quadrant points as the starting points of the regression for producing high-quality oriented candidates without any postprocessing. In the second stage, the final localization results are refined using the proposed novel bounding box representation, which can fully release the capabilities of the oriented detectors. Such consistency brings a good trade-off between accuracy and speed. With only flipping augmentation and single-scale training and testing, our approach with ResNet-50-FPN harvests 76.25% mAP on the DOTA dataset with a speed of up to 16.5 frames/s, achieving the best accuracy and the fastest speed among the mainstream two-stage oriented detectors. Additional results on the DIOR-R and HRSC2016 datasets also demonstrate the effectiveness and robustness of our method. The source code is publicly available athttps://github.com/yanqingyao1994/QPDet.

Oriented object detection is a practical and challenging task in remote sensing image interpretation. Nowadays, oriented detectors mostly use horizontal boxes as intermedium to derive oriented boxes from them. However, the horizontal boxes are inclined to get small Intersection-over-Unions (IoUs) with ground truths, which may have some undesirable effects, such as introducing redundant noise, mismatching with ground truths, detracting from the robustness of detectors, etc. In this paper, we propose a novel Anchor-free Oriented Proposal Generator (AOPG) that abandons horizontal box-related operations from the network architecture. AOPG first produces coarse oriented boxes by a Coarse Location Module (CLM) in an anchor-free manner and then refines them into high-quality oriented proposals. After AOPG, we apply a Fast R-CNN head to produce the final detection results. Furthermore, the shortage of large-scale datasets is also a hindrance to the development of oriented object detection. To alleviate the data insufficiency, we release a new dataset on the basis of our DIOR dataset and name it DIOR-R. Massive experiments demonstrate the effectiveness of AOPG. Particularly, without bells and whistles, we achieve the accuracy of 64.41%, 75.24% and 96.22% mAP on the DIOR-R, DOTA and HRSC2016 datasets respectively. Code and models are available at https://github.com/jbwang1997/AOPG.

In the past few years, object detection in remote sensing images has achieved remarkable progress. However, the detection of oriented and densely packed objects are still unsatisfactory due to the following spatial and feature misalignments. 1) Most two-stage oriented detectors only introduce an orientation regression branch in the detection head, while still leverage horizontal proposals for classification and regression. This inevitably results in the spatial misalignment problem between horizontal proposals and oriented objects. 2) The features used for classification are in fact extracted from the region proposals which have shifted to the final predictions via the regression branch. This leads to the feature misalignment problem between the classification and the localization tasks. In this article, we present a two-stage oriented object detection method, termed dual-aligned oriented detector (DODet), toward evading the aforementioned problems of spatial and feature misalignments. In DODet, the first stage is an oriented proposal network (OPN), which generates high-quality oriented proposals via a novel representation scheme of oriented objects. The second stage is a localization-guided detection head (LDH) that aims at alleviating the feature misalignment between classification and localization. Comprehensive and extensive evaluations on three benchmarks, including DIOR-R, DOTA, and HRSC2016, indicate that our method could obtain consistent and substantial gains compared with the baseline method. The source code is publicly available athttps://github.com/yanqingyao1994/DODet.

Current state-of-the-art two-stage detectors generate oriented proposals through time-consuming schemes. This diminishes the detectors’ speed, thereby becoming the computational bottleneck in advanced oriented object detection systems. This work proposes an effective and simple oriented object detection framework, termed Oriented R-CNN, which is a general two-stage oriented detector with promising accuracy and efficiency. To be specific, in the first stage, we propose an oriented Region Proposal Network (oriented RPN) that directly generates high-quality oriented proposals in a nearly cost-free manner. The second stage is oriented R-CNN head for refining oriented Regions of Interest (oriented RoIs) and recognizing them. Without tricks, oriented R-CNN with ResNet50 achieves state-of-the-art detection accuracy on two commonly-used datasets for oriented object detection including DOTA (75.87% mAP) and HRSC2016 (96.50% mAP), while having a speed of 15.1 FPS with the image size of 1024×1024 on a single RTX 2080Ti. We hope our work could inspire rethinking the design of oriented detectors and serve as a baseline for oriented object detection. Code is available at https://github.com/jbwang1997/OBBDetection.