Remote Objects Research Articles

A Refined and Efficient CNN Algorithm for Remote Sensing Object Detection

Remote sensing object detection (RSOD) plays a crucial role in resource utilization, geological disaster risk assessment and urban planning. Deep learning-based object-detection algorithms have proven effective in remote sensing image studies. However, accurate detection of objects with small size, dense distribution and complex object arrangement remains a significant challenge in the remote sensing field. To address this, a refined and efficient object-detection algorithm (RE-YOLO) has been proposed in this paper for remote sensing images. Initially, a refined and efficient module (REM) was designed to balance computational complexity and feature-extraction capabilities, which serves as a key component of the RE_CSP block. RE_CSP block efficiently extracts multi-scale information, overcoming challenges posed by complex backgrounds. Moreover, the spatial extracted attention module (SEAM) has been proposed in the bottleneck of backbone to promote representative feature learning and enhance the semantic information capture. In addition, a three-branch path aggregation network (TBPAN) has been constructed as the neck network, which facilitates comprehensive fusion of shallow positional information and deep semantic information across different channels, enabling the network with a robust ability to capture contextual information. Extensive experiments conducted on two large-scale remote sensing datasets, DOTA-v1.0 and SCERL, demonstrate that the proposed RE-YOLO outperforms state-of-the-art other object-detection approaches and exhibits a significant improvement in generalization ability.

Lock Operations Through the Operator’s Eyes: A Qualitative Exploration of Gaze Strategies

This paper reports a qualitative exploration of gaze strategies during closed-circuit television (CCTV) tasks in remote nautical object control of a lock. Previous research has not examined gaze strategies in scenarios where systems, such as nautical objects, are operated remotely using CCTV. As contextual factors matter in nautical object control, a qualitative approach was necessary to uncover domain-specific terminology and insights into gaze strategies. We recorded eye gaze from professional lock operators and then conducted semi-structured interviews with domain experts to assess these recordings. Thematic analysis revealed that experts were able to identify (features of) gaze strategies but did not share the same terminology. Based on this analysis we defined four strategies: anticipating, verifying, overview, and movement-directed gazes (RQ1). All strategies, except movement-directed gaze, were observed consistently across operators (RQ2), with verifying gaze aligning with task steps in a predefined protocol (RQ3). More generally, our classification framework from thematic analysis could help to systematically define and verify gaze strategies based on domain and task features across various CCTV working contexts. For nautical object control, the framework can be instrumental in interpreting and verifying future (quantitative) eye tracking results and informing instructional procedures.

DETR-ORD: An Improved DETR Detector for Oriented Remote Sensing Object Detection with Feature Reconstruction and Dynamic Query

Optical remote sensing images often feature high resolution, dense target distribution, and uneven target sizes, while transformer-based detectors like DETR reduce manually designed components, DETR does not support arbitrary-oriented object detection and suffers from high computational costs and slow convergence when handling large sequences of images. Additionally, bipartite graph matching and the limit on the number of queries result in transformer-based detectors performing poorly in scenarios with multiple objects and small object sizes. We propose an improved DETR detector for Oriented remote sensing object detection with Feature Reconstruction and Dynamic Query, termed DETR-ORD. It introduces rotation into the transformer architecture for oriented object detection, reduces computational cost with a hybrid encoder, and includes an IFR (image feature reconstruction) module to address the loss of positional information due to the flattening operation. It also uses ATSS to select auxiliary dynamic training queries for the decoder. This improved DETR-based detector enhances detection performance in challenging oriented optical remote sensing scenarios with similar backbone network parameters. Our approach achieves superior results on most optical remote sensing datasets, such as DOTA-v1.5 (72.07% mAP) and DIOR-R (66.60% mAP), surpassing the baseline detector.

Setting up an institutional OMERO environment for bioimage data: Perspectives from both facility staff and users.

Modern bioimaging core facilities at research institutions are essential for managing and maintaining high-end instruments, providing training and support for researchers in experimental design, image acquisition and data analysis. An important task for these facilities is the professional management of complex multidimensional bioimaging data, which are often produced in large quantity and very different file formats. This article details the process that led to successfully implementing the OME Remote Objects system (OMERO) for bioimage-specific research data management (RDM) at the Core Facility Cellular Imaging (CFCI) at the Technische Universität Dresden (TU Dresden). Ensuring compliance with the FAIR (findable, accessible, interoperable, reusable) principles, we outline here the challenges that we faced in adapting data handling and storage to a new RDM system. These challenges included the introduction of a standardised group-specific naming convention, metadata curation with tagging and Key-Value pairs, and integration of existing image processing workflows. By sharing our experiences, this article aims to provide insights and recommendations for both individual researchers and educational institutions intending to implement OMERO as a management system for bioimaging data. We showcase how tailored decisions and structured approaches lead to successful outcomes in RDM practices.

Improved unmanned aerial vehicle control for efficient obstacle detection and data protection

<p>The article centers on the research objectives and tasks associated with developing a swarm control system for unmanned aerial vehicles (UAVs) utilizing artificial intelligence (AI). A comprehensive literature review was undertaken to assess the effectiveness of the "swarm" method in UAV management and identify key challenges in this domain. Swarm algorithms were implemented in the MATLAB/Simulink environment for modeling and simulation purposes. The study successfully instantiated and simulated a UAV swarm control system adhering to fundamental principles and laws. Each UAV operates autonomously, following target-swarm principles inspired by the collective behavior of bees and ants. The collective movement and behavior of the swarm are controlled by an AI-based program. The system demonstrated effective obstacle detection and avoidance through computer simulations. Results obtained highlight key features contributing to success, including decentralized autonomy, collective intelligence, UAV coordination, scalability, and flexibility. The deployment of a local radio communication system in UAV swarm control and remote object monitoring is also discussed. The research findings hold practical significance as they enable the effective execution of complex tasks and have potential applications in various fields.</p>

Spatial Augmented Reality for Expanding the Reach of Individuals with Tremor beyond Their Physical Limits.

Tremor is a prevalent neurological disorder characterized by involuntary shaking or trembling of body parts. This condition impairs fine motor skills and hand coordination to varying degrees and can even affect overall body mobility. As a result, tremors severely disrupt the daily lives and work of those affected, significantly limiting their physical activity space. This study developed an innovative spatial augmented reality (SAR) system aimed at assisting individuals with tremor disorders to overcome their physical limitations and expand their range of activities. The system integrates eye-tracking and Internet of Things (IoT) technologies, enabling users to smoothly control objects in the real world through eye movements. It uses a virtual stabilization algorithm for stable interaction with objects in the real environment. The study comprehensively evaluated the system's performance through three experiments: (1) assessing the effectiveness of the virtual stabilization algorithm in enhancing the system's ability to assist individuals with tremors in stable and efficient interaction with remote objects, (2) evaluating the system's fluidity and stability in performing complex interactive tasks, and (3) investigating the precision and efficiency of the system in remote interactions within complex physical environments. The results demonstrated that the system significantly improves the stability and efficiency of interactions between individuals with tremor and remote objects, reduces operational errors, and enhances the accuracy and communication efficiency of interactions.

Enhancing Remote Sensing Object Detection with K-CBST YOLO: Integrating CBAM and Swin-Transformer

Enhancing Remote Sensing Object Detection with K-CBST YOLO: Integrating CBAM and Swin-Transformer

Remote refocusing for multi-scale imaging.

The technique of remote focusing (RF) has attracted considerable attention among microscopists due to its ability to quickly adjust focus across different planes, thus facilitating quicker volumetric imaging. However, the difficulty in changing objectives to align with a matching objective in a remote setting while upholding key requirements remains a challenge. We aim to propose a customized yet straightforward technique to align multiple objectives with a remote objective, employing an identical set of optical elements to ensure meeting the criteria of remote focusing. We propose a simple optical approach for aligning multiple objectives with a singular remote objective to achieve a perfect imaging system. This method utilizes readily accessible, commercial optical components to meet the fundamental requirements of remote focusing. Our experimental observations indicate that the proposed RF technique offers at least comparable, if not superior, performance over a significant axial depth compared with the conventional RF technique based on commercial lenses while offering the flexibility to switch the objective for multi-scale imaging. The proposed technique addresses various microscopy challenges, particularly in the realm of multi-resolution imaging. We have experimentally demonstrated the efficacy of this technique by capturing images of focal volumes generated by two distinct objectives in a water medium.

BiF-DETR:Remote sensing object detection based on Bidirectional information fusion

Remote Sensing Object Detection(RSOD) is a fundamental task in the field of remote sensing image processing. The complexity of the background, the diversity of object scales and the locality limitation of Convolutional Neural Network (CNN) present specific challenges for RSOD. In this paper, an innovative hybrid detector, Bidirectional Information Fusion DEtection TRansformer (BiF-DETR), is proposed to mitigate the above issues. Specifically, BiF-DETR takes anchor-free detection network, CenterNet, as the baseline, designs the feature extraction backbone in parallel, extracts the local feature details using CNNs, and obtains the global information and long-term dependencies using Transformer branch. A Bidirectional Information Fusion (BIF) module is elaborately designed to reduce the semantic differences between different styles of feature maps through multi-level iterative information interactions, fully utilizing the complementary advantages of different detectors. Additionally, Coordination Attention(CA), is introduced to enables the detection network to focus on the saliency information of small objects. To address diversity insufficiency of remote sensing images in the training stage, Cascade Mixture Data Augmentation (CMDA), is designed to improve the robustness and generalization ability of the model. Comparative experiments with other cutting-edge methods are conducted on the publicly available DOTA and NWPU VHR-10 datasets. The experimental results reveal that the performance of proposed method is state-of-the-art, with mAP reaching 77.43% and 94.75%, respectively, far exceeding the other 25 competitive methods.

Implementation features of local and remote technical objects digital twins

Implementation features of local and remote technical objects digital twins

Avian eye-inspired perovskite artificial vision system for foveated and multispectral imaging.

Avian eyes have deep central foveae as a result of extensive evolution. Deep foveae efficiently refract incident light, creating a magnified image of the target object and making it easier to track object motion. These features are essential for detecting and tracking remote objects in dynamic environments. Furthermore, avian eyes respond to a wide spectrum of light, including visible and ultraviolet light, allowing them to efficiently distinguish the target object from complex backgrounds. Despite notable advances in artificial vision systems that mimic animal vision, the exceptional object detection and targeting capabilities of avian eyes via foveated and multispectral imaging remain underexplored. Here, we present an artificial vision system that capitalizes on these aspects of avian vision. We introduce an artificial fovea and vertically stacked perovskite photodetector arrays whose designs were optimized by theoretical simulations for the demonstration of foveated and multispectral imaging. The artificial vision system successfully identifies colored and mixed-color objects and detects remote objects through foveated imaging. The potential for use in uncrewed aerial vehicles that need to detect, track, and recognize distant targets in dynamic environments is also discussed. Our avian eye-inspired perovskite artificial vision system marks a notable advance in bioinspired artificial visions.

Seduction of far-right actions: A pathway to an authentic self?

Far-right civil movements have emerged as a significant predicament in numerous regions worldwide. Despite abundant research on the tactics employed by far-right groups to instil ideologies, behaviours and sentiments in their followers, such as street demonstrations, investigations into the meaning-making of far-right actions in relation to the trajectories of the participants’ lives remain scarce. The present study uses the analytical tool proposed by Katz to explore how far-right activists obtain a sense of moral transcendence through their activism, including participating in the liminal moments of far-right rallies, revelling in unrestricted speech, discovering new abilities, acquiring knowledge and engaging in self-expression. We contend that these experiences engender in participants the sense that they are acquiring that which is absent from their lives and recovering their ‘true selves’. The study maintains that to comprehend the allure of the far right, it is essential to contextualise far-right actions within the participants’ lives, the societies that they inhabit, and the circumstances that surround them. In short, the sensation that one is rediscovering one’s ‘true self’ may encourage activists to pursue the more remote objective of reclaiming what they perceive as their nations’ lost ideals.

МАТЕМАТИЧНІ МОДЕЛІ ТА МЕТОДИ РОЗМІЩЕННЯ ОБ’ЄКТІВ І ЗОНУВАННЯ ТЕРИТОРІЙ В СИСТЕМАХ ЕКСТРЕНОЇ ЛОГІСТИКИ

The mathematical models for distribution processes related to organizing precautionary measures in the event of threats or occurrences of man-made emergencies are presented. The tasks include optimal zoning of territories with the fixing of zones by objects of social purpose for service provision. Provision is made for: the possibility of overlapping zones in case the nearest center cannot provide the service; optimal placement of a certain number of new cen-ters of emergency logistics systems with simultaneous redistribution of the load on all their structural elements; the selection of locations of structural subdivisions based on existing fa-cilities. The optimality criteria involve minimizing either the time to provide the service even to the most remote object in the given territory, or the total distance to the nearest centers from consumers that are densely distributed in the given territory, and/or the organizational costs associated with the arrangement of new centers. Mathematical models are proposed in the form of continuous problems of optimal multiplex partitioning of sets with a linear or minimax functional of quality. The latter provides such placement of centers that provides op-timal multiple coverage of the territory (with a minimum radius of multiple coverage). Meth-ods for solving the formulated problems were developed using LP-relaxation of linear prob-lems with Boolean variables, duality theory to reduce the initial problems of infinite-dimensional programming to problems of conditional optimization of a non-smooth function of several variables, and modern methods of non-differentiated optimization.

Augmented Commonsense Knowledge for Remote Object Grounding

The vision-and-language navigation (VLN) task necessitates an agent to perceive the surroundings, follow natural language instructions, and act in photo-realistic unseen environments. Most of the existing methods employ the entire image or object features to represent navigable viewpoints. However, these representations are insufficient for proper action prediction, especially for the REVERIE task, which uses concise high-level instructions, such as “Bring me the blue cushion in the master bedroom”. To address enhancing representation, we propose an augmented commonsense knowledge model (ACK) to leverage commonsense information as a spatio-temporal knowledge graph for improving agent navigation. Specifically, the proposed approach involves constructing a knowledge base by retrieving commonsense information from ConceptNet, followed by a refinement module to remove noisy and irrelevant knowledge. We further present ACK which consists of knowledge graph-aware cross-modal and concept aggregation modules to enhance visual representation and visual-textual data alignment by integrating visible objects, commonsense knowledge, and concept history, which includes object and knowledge temporal information. Moreover, we add a new pipeline for the commonsense-based decision-making process which leads to more accurate local action prediction. Experimental results demonstrate our proposed model noticeably outperforms the baseline and archives the state-of-the-art on the REVERIE benchmark. The source code is available at https://github.com/Bahram-Mohammadi/ACK.

METHODOLOGY FOR ASSESSING THE EFFECTIVENESS OF THE FUNCTIONING OF OPTICAL-ELECTRONIC SYSTEMS WHEN MONITORING REMOTE OBJECTS

The work proposes an approach to improving the scientific and methodological apparatus for processing information about observed distant objects. The potential possibility of increasing the accuracy of measuring the coordinates of remote objects and obtaining spectrophotometric information through a refined mathematical model of the process of functioning of optical-electronic systems, based on the possibility of restoring the original signal from the observed object by solving the Fredholm integral equation of the 1st kind, is shown. Based on a mathematical model that takes into account the structure of the optical circuit, the influence of noise and external observation conditions, the methodology for assessing the effectiveness of optical-electronic systems when observing remote objects has been improved.

RADAR-BASED DETECTION AND RECOGNITION METHODOLOGY OF AUTONOMOUS SURFACE VEHICLES IN CHALLENGING MARINE ENVIRONMENT

This paper presents a methodology that combines radar polarization selection and recognition techniques for navigating objects in atmospheric formations, with a special focus on unmanned surface vehicles (ASVs). The proposed technique utilizes the concept of an energy dissipation matrix to represent these objects as characteristic “shiny dots”. By strategically changing the polarization of the emitted and received electromagnetic waves, the resulting echo energy dissipation matrix is determined. This approach allows the formation of an intensity-based repository of atmospheric formations, which gives SRPC a complete set of tools to account for atmospheric conditions in radar identification of remote objects, including ASVs. The practical application of this technique extends to the improvement of a distinct class of shipborne radar systems optimized for ASVs and their specific navigation requirements. Ultimately, this technology bridges the gap between advanced radar technology and the emerging field of unmanned ground vehicles, providing safer and more proficient navigation in challenging weather conditions.

Study of methods for searching and localizing objects in images from aircraft using convolutional neural networks

The use of unmanned and manned aerial vehicles for remote object localization and classification is very common. These methods are used in various systems, ranging from territory surveys to law enforcement. Methods of object localization and classification using neural networks require a detailed study and research of the quality of their work on data that has certain specifics, such as vehicle detection. The use of neural networks to detect certain types of objects using images obtained from aircraft can also help in the study of hard-to-reach locations. Therefore, the main subject of this paper is the localization and classification of objects in images obtained using digital cameras mounted on aircraft. The main focus is on determining the accuracy of object localization and detection using selected types of neural networks, which are the most important indicators of neural network efficiency. The speed of a neural network is also an equally important characteristic as it directly affects its ability to be used in tasks that require fast object localization, such as video surveillance or automated car control systems. The main goal of this study is to study the accuracy of object localization and classification in images obtained with the help of cameras mounted on aircraft, as well as to study the speed of neural networks and determine the effectiveness of their application in real-world conditions. The objectives of this study are to train YOLO v5, SSD, and Faster RCNNs on the VisDrone dataset and to further study them on the vehicle localization dataset. The main goal of this work is to obtain statistics on the performance of neural networks trained on the VisDrone dataset. On the basis of the obtained statistics, conclusions are drawn about the effectiveness of the considered neural networks. The conclusions are drawn by considering the speed of the model, localization (IoU), and classification (Precision, Recall) metrics. Possible directions for further development of the topic under study are presented as conclusions.

OII: An Orientation Information Integrating Network for Oriented Object Detection in Remote Sensing Images

Oriented object detection for remote sensing images poses formidable challenges due to arbitrary orientation, diverse scales, and densely distributed targets (e.g., across terrain). Current investigations in remote sensing object detection have primarily focused on improving the representation of oriented bounding boxes yet have neglected the significant orientation information of targets in remote sensing contexts. Recent investigations point out that the inclusion and fusion of orientation information yields substantial benefits in training an accurate oriented object system. In this paper, we propose a simple but effective orientation information integrating (OII) network comprising two main parts: the orientation information highlighting (OIH) module and orientation feature fusion (OFF) module. The OIH module extracts orientation features from those produced by the backbone by modeling the frequency information of spatial features. Given that low-frequency components in an image capture its primary content, and high-frequency components contribute to its intricate details and edges, the transformation from the spatial domain to the frequency domain can effectively emphasize the orientation information of images. Subsequently, our OFF module employs a combination of a CNN attention mechanism and self-attention to derive weights for orientation features and original features. These derived weights are adopted to adaptively enhance the original features, resulting in integrated features that contain enriched orientation information. Given the inherent limitation of the original spatial attention weights in explicitly capturing orientation nuances, the incorporation of the introduced orientation weights serves as a pivotal tool to accentuate and delineate orientation information related to targets. Without unnecessary embellishments, our OII network achieves competitive detection accuracy on two prevalent remote sensing-oriented object detection datasets: DOTA (80.82 mAP) and HRSC2016 (98.32 mAP).

Center-Wise Feature Consistency Learning for Long-Tailed Remote Sensing Object Recognition

Center-Wise Feature Consistency Learning for Long-Tailed Remote Sensing Object Recognition

Transferable Adversarial Attacks for Remote Sensing Object Recognition via Spatial- Frequency Co-Transformation

Transferable Adversarial Attacks for Remote Sensing Object Recognition via Spatial- Frequency Co-Transformation