[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4220":3,"related-4220":88},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":18,"tags":20,"primary_topic":9,"secondary_topics":25,"classification":26,"search_phrases":60,"slug":63,"view_count":15,"doi":9,"paper":64,"created_at":87},4220,"Системи на основі YOLO для виявлення вибухонебезпечних залишків війни: огляд для гуманітарного розмінування","https:\u002F\u002Fais.khpi.edu.ua\u002Farticle\u002Fview\u002F372728","Relevance. Humanitarian demining remains a critical safety challenge because explosive remnants of war, landmines, and unexploded ordnance continue to endanger civilians and hinder post-conflict recovery. In this context, artificial intelligence and computer vision are increasingly used to support hazardous-object detection in UAVs-based survey operations. The purpose of this paper is to synthesize contemporary approaches to the detection of landmines, unexploded ordnance, and other explosive remnants of war, and to define the role of YOLO models in an integrated technological pipeline for surveying hazardous areas. The object of the review is technological solutions for detecting hazardous objects using UAVs, sensor systems, geoinformation technologies, and YOLO-family models. The subject of the paper is the application of YOLO-based systems in integrated pipelines for localizing potential explosive objects from visual and spectral data. Research results. The review shows that modern approaches combine ground-penetrating radar, magnetometry, thermal sensing, multispectral imaging, hyperspectral imaging, UAV-based inspection, GIS platforms, and edge\u002Fcloud architectures. YOLO demonstrates strong potential for rapid localization of visually or spectrally expressed indicators of hazardous objects, especially when combined with other sensing modalities. Conclusions. At the same time, YOLO cannot serve as the sole basis for declaring an area safe. Its use is constrained by small target size, camouflage, domain shift, limited annotated datasets, and the high cost of false negatives. Therefore, YOLO-based detection should be treated as one component of a multisensor, human-supervised decision-support system.","相关性。人道主义排雷仍然是一项关键的安全挑战，因为战争遗留爆炸物、地雷和未爆弹药继续危及平民并阻碍冲突后恢复。在此背景下，人工智能和计算机视觉越来越多地被用于支持基于无人机（UAV）的勘察作业中的危险物体检测。本文旨在综合当前对地雷、未爆弹药和其他战争遗留爆炸物的检测方法，并明确YOLO模型在危险区域勘察集成技术流程中的作用。综述的对象是利用无人机、传感器系统、地理信息技术和YOLO系列模型检测危险物体的技术解决方案。论文的主题是基于YOLO的系统在集成流程中的应用，用于从视觉和光谱数据中定位潜在爆炸物体。研究结果。综述表明，现代方法结合了探地雷达、磁力测量、热传感、多光谱成像、高光谱成像、基于无人机的检查、GIS平台以及边缘\u002F云架构。YOLO在快速定位视觉或光谱上可表现出的危险物体指标方面展现出强大潜力，尤其是与其他传感模态结合使用时。结论。与此同时，YOLO不能作为宣布某一区域安全的唯一依据。其使用受到小目标尺寸、伪装、域偏移、标注数据集有限以及假阴性高成本的制约。因此，基于YOLO的检测应被视为多传感器、人工监督决策支持系统的一个组成部分。",null,"The Scientific Issues of Ternopil Volodymyr Hnatiuk National Pedagogical University Series pedagogy","2026-10-02T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"主题为人道主义排雷中的YOLO目标检测综述，属军事\u002F人道安全领域，与三农及农业信息化无直接关联，不予入选。",[19],{"name":10,"url":6},[21,22,23,24],"无人机","农业人工智能","目标检测","遥感",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":30},"needs_review","2026-10-22.2","rules",{"candidates":31,"agricultural_evidence":14,"reason":59},[32,43,51],{"code":33,"score":34,"supported":35,"title":36,"abstract":38,"tags":42},"I2",19,true,[37],"YOLO",[39,40,41,37],"计算机视觉","artificial intelligence","computer vision",[22,23],{"code":44,"score":45,"supported":35,"title":46,"abstract":47,"tags":50},"I3",7,[],[48,49],"multispectral","hyperspectral",[24],{"code":52,"score":53,"supported":35,"title":54,"abstract":55,"tags":58},"I4",6,[],[56,57],"地理信息","GIS",[],"农业相关性需要复核",[61,62],"YOLO 排雷 无人机","人道主义排雷 遥感","YOLO排雷无人机-4220",{"doi":9,"openalex_id":65,"authors":66,"venue":10,"cited_by_count":15,"oa_url":6,"card":79,"direction":85,"ingested_from":86},"W7218146160",[67,69,72,74,77],{"name":68,"orcid":9},"Andrii Podorozhniak",{"name":70,"orcid":71},"Денис Левченко","https:\u002F\u002Forcid.org\u002F0009-0005-4102-6236",{"name":73,"orcid":9},"Nataliia Liubchenko",{"name":75,"orcid":76},"Іслам Ісламов","https:\u002F\u002Forcid.org\u002F0000-0001-8645-0640",{"name":78,"orcid":9},"Oleksandr Skorlupin",{"tldr":80,"method":81,"finding":82,"direction":83,"opportunity":84},"综述YOLO模型在无人机人道排雷中检测战争爆炸残留物的应用与局限。","综述探地雷达、磁力、热成像、多\u002F高光谱、无人机、GIS与YOLO集成方案。","YOLO可快速定位危险目标，但受小目标、伪装、域偏移、数据少和高漏检代价限制，不能单独判定区域安全。","农业遥感与作物表型","可探索多源遥感与YOLO融合的轻量边缘检测，并构建农业场景下小目标、域偏移的鲁棒性评估基准。","智慧农业 \u002F 农业物联网","openalex","2026-10-05T23:30:12.187482Z",{"total":53,"page":89,"page_size":53,"items":90},1,[91,154,226,287,360,411],{"id":92,"title":93,"url":94,"summary":95,"summary_zh":96,"content":9,"source_name":97,"source_url":94,"published_at":98,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":99,"score_detail":100,"sources":107,"tags":109,"primary_topic":111,"secondary_topics":116,"classification":117,"search_phrases":131,"slug":134,"view_count":105,"doi":135,"paper":136,"created_at":153},3503,"Adversarial Patch and Camouflage Attacks on Aerial Object Detection: A Geometry-Aware Review","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs26196057","Uncrewed aerial vehicles increasingly rely on on-board deep object detectors for applications such as surveillance, delivery, agriculture, and traffic monitoring. The vulnerability of these detectors to adversarial patches has therefore become a practical safety concern. Research on adversarial patch and camouflage attacks against aerial object detection has expanded rapidly, yet existing reviews consider aerial systems only as one application domain among many and do not examine the unique geometric conditions of aerial imagery. This review surveys adversarial patch and camouflage attacks, together with the corresponding defense mechanisms, for aerial, UAV, and remote sensing object detection. The literature is organized using a taxonomy based on two dimensions: the physical medium of the perturbation and the application domain it targets. Existing methods are then compared with respect to physical robustness, evaluation datasets and detectors, threat models, and their treatment of viewpoint variation. A recurring observation across the literature is that viewpoint variation is represented primarily through object scale, while explicit modeling of viewing geometry remains limited. This trend is closely linked to the characteristics of the aerial datasets used for evaluation, which typically provide realistic imagery but limited geometric information. Consequently, questions concerning viewpoint-dependent placement, degradation, and naturalness remain only partially explored. The review concludes by identifying the major research gaps, future directions, and deployment challenges that are likely to shape the next generation of aerial adversarial attack and defense research.","无人驾驶飞行器（UAV）在监视、配送、农业和交通监测等应用中日益依赖机载深度目标检测器。因此，这些检测器对对抗性补丁的脆弱性已成为一个实际的安全问题。针对空中目标检测的对抗性补丁与伪装攻击研究迅速扩展，然而现有综述仅将空中系统视为众多应用领域之一，并未考察航空影像独特的几何条件。本综述考察了针对空中、无人机及遥感目标检测的对抗性补丁与伪装攻击以及相应的防御机制。文献按照基于两个维度的分类体系进行组织：扰动的物理介质及其所针对的应用领域。随后，现有方法在物理鲁棒性、评估数据集与检测器、威胁模型以及其对视角变化的处理方面进行了比较。文献中反复出现的一个观察是，视角变化主要通过目标尺度来表示，而对观察几何的显式建模仍然有限。这一趋势与用于评估的航空数据集的特征密切相关，这些数据集通常提供真实影像但几何信息有限。因此，关于视角依赖的放置、退化及自然性的问题仅得到部分探讨。本综述最后指出了可能塑造下一代空中对抗攻击与防御研究的主要研究空白、未来方向及部署挑战。","Sensors","2026-09-24T00:00:00Z",67,{"impact":101,"substance":102,"depth":103,"authority":104,"freshness":105,"relevant":89,"comment":106},8,20,17,13,9,"系统梳理航空与无人机目标检测的对抗补丁与伪装攻击及防御，指出视角几何建模不足的研究空白，对农业遥感与无人机应用安全有参考价值。",[108],{"name":97,"url":94},[21,22,23,24,110],"对抗攻击",{"code":44,"slug":112,"name":113,"path":114,"parent_code":115},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[],{"status":118,"locked":14,"version":28,"method":29,"evidence":119},"classified",{"candidates":120,"agricultural_evidence":35},[121,126],{"code":44,"score":45,"supported":35,"title":122,"abstract":123,"tags":125},[],[24,124],"remote sensing",[24],{"code":33,"score":127,"supported":35,"title":128,"abstract":129,"tags":130},5,[],[23],[22,23],[132,133],"无人机 对抗补丁 目标检测","航空图像 对抗攻击","无人机对抗补丁目标检测-3503","10.3390\u002Fs26196057",{"doi":135,"openalex_id":137,"authors":138,"venue":97,"cited_by_count":15,"oa_url":94,"card":148,"direction":83,"ingested_from":86},"W7214217753",[139,142,145],{"name":140,"orcid":141},"Sandesh Shrestha","https:\u002F\u002Forcid.org\u002F0000-0001-5298-4468",{"name":143,"orcid":144},"K. T. Y. Mahima","https:\u002F\u002Forcid.org\u002F0000-0003-4975-9408",{"name":146,"orcid":147},"Asanka G. Perera","https:\u002F\u002Forcid.org\u002F0000-0003-4021-3943",{"tldr":149,"method":150,"finding":151,"direction":83,"opportunity":152},"综述无人机与遥感目标检测中的对抗补丁与伪装攻击及防御，并提出几何感知分类体系。","基于扰动介质与应用领域的分类法，比较物理鲁棒性、数据集、威胁模型与视角处理。","现有研究多以目标尺度代替视角变化，缺乏显式视角几何建模，因数据集几何信息有限。","可构建含视角几何标注的农业航拍数据集，研究视角依赖的对抗补丁放置与自然性防御。","2026-09-25T23:30:31.219576Z",{"id":155,"title":156,"url":157,"summary":158,"summary_zh":159,"content":9,"source_name":160,"source_url":157,"published_at":161,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":162,"score_detail":163,"sources":168,"tags":170,"primary_topic":172,"secondary_topics":176,"classification":177,"search_phrases":196,"slug":199,"view_count":15,"doi":200,"paper":201,"created_at":225},5393,"IMPLEMENTATION OF META-HEURISTIC OPTIMIZATION ALGORITHMS IN CONVOLUTIONAL NEURAL NETWORKS FOR OBJECT DETECTION AND CLASSIFICATION","https:\u002F\u002Fdoi.org\u002F10.22190\u002Ffuacr251214006i","CNNs have grown to form the backbone of modern computer vision systems, producing outstanding object detection and classification performance within diverse application domains. However, the manual development of suitable CNN architectures and, correspondingly, the selection of optimal hyperparameter sets are still inconvenient and time-consuming processes. The aim of this survey is to contribute a comprehensive review regarding how meta-heuristic optimization algorithms such as Genetic Algorithms, Particle Swarm Optimization, and other swarm intelligence-based methods are embedded in the CNN-based pipelines with the intent to achieve better detection and classification results. We gather recent progress regarding automated architecture search, hyperparameter tuning, model compression, and ensemble learning. We identify how hybrid optimization approaches consistently yield superior accuracy with computational efficiency by systematically reviewing applications in medical imaging, agriculture, vehicular recognition, and remote sensing. Optimized CNN models outperform their baseline versions many times, especially in health diagnostics, where several fine-tuned architectures reach accuracies greater than 99%. This survey provides a structured and updated overview of methodological developments, implementation practices, and emerging research directions for those practitioners and researchers who will apply meta-heuristic optimization within deep learning-based computer vision systems.","卷积神经网络（CNNs）已发展成为现代计算机视觉系统的核心骨干，在多个应用领域中展现出卓越的目标检测与分类性能。然而，人工设计合适的CNN架构并相应地选择最优超参数集仍然是繁琐且耗时的过程。本综述旨在全面回顾遗传算法、粒子群优化及其他群体智能方法等元启发式优化算法如何嵌入基于CNN的流程中以获得更优的检测与分类结果。我们汇集了自动化架构搜索、超参数调优、模型压缩和集成学习方面的最新进展。通过系统梳理在医学影像、农业、车辆识别和遥感领域的应用，我们揭示了混合优化方法如何以计算效率持续实现更优的精度。优化后的CNN模型在多数情况下远超其基线版本，尤其在健康诊断领域，若干精细调优的架构达到了超过99%的准确率。本综述为将在基于深度学习的计算机视觉系统中应用元启发式优化的从业者和研究者，提供了关于方法学进展、实施实践和新兴研究方向的结构化且更新的概述。","Facta Universitatis Series Automatic Control and Robotics","2026-10-18T00:00:00Z",61,{"impact":101,"substance":164,"depth":165,"authority":166,"freshness":101,"relevant":89,"comment":167},18,15,12,"综述系统梳理元启发式优化在CNN视觉任务中的嵌入方法，含农业应用，但属通用方法综述，非农业专属突破。",[169],{"name":160,"url":157},[171,22,23,24],"智慧农业",{"code":33,"slug":173,"name":174,"path":175,"parent_code":115},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",[],{"status":118,"locked":14,"version":28,"method":29,"evidence":178},{"candidates":179,"agricultural_evidence":35},[180,187,191],{"code":33,"score":181,"supported":35,"title":182,"abstract":183,"tags":186},11,[],[23,184,39,185,41],"深度学习","deep learning",[22,23],{"code":44,"score":45,"supported":35,"title":188,"abstract":189,"tags":190},[],[24,124],[24],{"code":192,"score":89,"supported":14,"title":193,"abstract":194,"tags":195},"I1",[],[],[171],[197,198],"元启发式优化 卷积神经网络","CNN 目标检测","元启发式优化卷积神经网络-5393","10.22190\u002Ffuacr251214006i",{"doi":200,"openalex_id":202,"authors":203,"venue":160,"cited_by_count":15,"oa_url":157,"card":219,"direction":83,"ingested_from":86},"W7222920697",[204,207,210,213,216],{"name":205,"orcid":206},"Nikola Ivačko","https:\u002F\u002Forcid.org\u002F0000-0003-4367-119X",{"name":208,"orcid":209},"Ivan S Ciric","https:\u002F\u002Forcid.org\u002F0000-0003-0430-8937",{"name":211,"orcid":212},"Milica Tasić","https:\u002F\u002Forcid.org\u002F0009-0001-9160-6379",{"name":214,"orcid":215},"Ljiljana Radović","https:\u002F\u002Forcid.org\u002F0000-0002-4099-1289",{"name":217,"orcid":218},"Žarko Ćojbašić","https:\u002F\u002Forcid.org\u002F0000-0002-4581-1048",{"tldr":220,"method":221,"finding":222,"direction":223,"opportunity":224},"综述元启发式算法嵌入CNN流程以优化目标检测与分类的研究进展。","系统综述遗传算法、粒子群等群智能方法用于CNN架构搜索与超参调优。","混合优化方法在医学影像、农业等领域兼顾精度与效率，部分模型准确率超99%。","农业人工智能与决策模型","农业场景下元启发式优化CNN的架构搜索与轻量化部署仍缺乏系统基准与田间验证。","2026-10-20T23:30:19.233167Z",{"id":227,"title":228,"url":229,"summary":230,"summary_zh":9,"content":9,"source_name":231,"source_url":229,"published_at":232,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":233,"score_detail":234,"sources":238,"tags":240,"primary_topic":242,"secondary_topics":246,"classification":247,"search_phrases":269,"slug":272,"view_count":89,"doi":273,"paper":274,"created_at":286},4973,"Barley head detection using UAV imagery and YOLOv10","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10468-w","Barley head detection using UAV imagery and YOLOv10。Precision Agriculture","Precision Agriculture","2026-10-14T00:00:00Z",75,{"impact":235,"substance":102,"depth":103,"authority":236,"freshness":101,"relevant":89,"comment":237},16,14,"将YOLOv10用于无人机大麦穗检测，方法新颖、数据可靠，属细分领域技术进展，值得精选。",[239],{"name":231,"url":229},[171,21,22,24,241],"大麦",{"code":192,"slug":243,"name":244,"path":245,"parent_code":115},"smart-agriculture","智慧农业总论","\u002Ftopic\u002Fdigital-agriculture\u002Fsmart-agriculture",[],{"status":118,"locked":14,"version":28,"method":29,"evidence":248},{"candidates":249,"agricultural_evidence":35},[250,256,261,265],{"code":192,"score":251,"supported":35,"title":252,"abstract":253,"tags":255},4,[],[254],"precision agriculture",[171],{"code":257,"score":89,"supported":14,"title":258,"abstract":259,"tags":260},"A5",[],[],[241],{"code":33,"score":89,"supported":14,"title":262,"abstract":263,"tags":264},[],[],[22],{"code":44,"score":89,"supported":14,"title":266,"abstract":267,"tags":268},[],[],[24],[270,271],"无人机 大麦 穗检测","YOLOv10 大麦 遥感","无人机大麦穗检测-4973","10.1007\u002Fs11119-026-10468-w",{"doi":273,"openalex_id":275,"authors":276,"venue":231,"cited_by_count":15,"oa_url":9,"card":9,"direction":9,"ingested_from":86},"W7221758985",[277,280,283],{"name":278,"orcid":279},"Ameer Tamoor Khan","https:\u002F\u002Forcid.org\u002F0000-0001-6838-992X",{"name":281,"orcid":282},"Saiful Azim","https:\u002F\u002Forcid.org\u002F0000-0002-2686-7150",{"name":284,"orcid":285},"Signe Marie Jensen","https:\u002F\u002Forcid.org\u002F0000-0002-7738-5231","2026-10-15T23:30:02.421798Z",{"id":288,"title":289,"url":290,"summary":291,"summary_zh":292,"content":9,"source_name":293,"source_url":290,"published_at":294,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":295,"score_detail":296,"sources":298,"tags":300,"primary_topic":302,"secondary_topics":303,"classification":305,"search_phrases":333,"slug":336,"view_count":15,"doi":337,"paper":338,"created_at":359},4505,"Application of Deep Learning Techniques for Vineyard Mapping and Characterization Using Multispectral UAV Imagery: A Case Study from Northern Greece","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23232412","Developments in unmanned aerial vehicle (UAV) technology and the rise of Deep Learning (DL) have drastically boosted the capacities of precision viticulture to monitor the environments of farms in a scalable and detailed manner. Vineyard mappings and extraction of vine-specific features are crucial for yield estimation, plant health assessment, and management decisions. This study examines the application of Deep Learning-based image analysis techniques for vineyard mapping and characterization using multispectral UAV image data (Deep Learning). The proposed approach integrates spatial and spectral information to delineate vineyard structures and capture key canopy characteristics relevant to precision agriculture. Multimodal datasets, including RGB and multispectral bands, facilitate improved discrimination between vines and surrounding elements. Geospatial analysis and data processing are further performed through ArcGIS Pro, enabling spatial validation of results, identifying extracted features, and creating appropriate interpretative thematic layers. The combination of UAV imagery, DL tools and GIS based analysis offers a strong and flexible workflow that can be adjusted for different grape cultivars, growth conditions and circumstances. Overall, the study demonstrates the potential benefits of the rigid union of UAV-based remote sensing with Deep Learning and GIS techniques to create a flexible, operational, and data-driven framework for vineyard management. Based on this, sustainable and fruitful management practices can be elaborated, thus optimizing resources and vineyard management decisions in precision viticulture,underlining the importance of such a technological integration for monitoring and managing modern vineyards.","无人机（UAV）技术的发展与深度学习（DL）的兴起，极大地提升了精准葡萄栽培以可扩展且精细的方式监测农场环境的能力。葡萄园制图与葡萄植株特异性特征的提取对于产量估算、植株健康评估和管理决策至关重要。本研究探讨了基于深度学习的图像分析技术在多光谱无人机影像数据（深度学习）用于葡萄园制图与表征中的应用。所提出的方法整合了空间与光谱信息，以 delineate 葡萄园结构并捕捉与精准农业相关的关键冠层特征。多模态数据集（包括RGB和多光谱波段）有助于更好地区分葡萄藤与周围元素。通过ArcGIS Pro进一步进行地理空间分析与数据处理，从而实现结果的空间验证、提取特征的识别以及适当的解释性专题图层的创建。无人机影像、深度学习工具与基于GIS的分析相结合，提供了一套强大且灵活的工作流程，可针对不同葡萄品种、生长条件和环境进行调整。总体而言，本研究展示了将基于无人机的遥感与深度学习和GIS技术 rigidly 结合，以创建灵活、可操作且数据驱动的葡萄园管理框架的潜在优势。在此基础上，可制定可持续且富有成效的管理实践，从而优化精准葡萄栽培中的资源与葡萄园管理决策，凸显了这种技术整合对现代葡萄园监测与管理的重要性。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-08T00:00:00Z",68,{"impact":166,"substance":164,"depth":235,"authority":104,"freshness":105,"relevant":89,"comment":297},"希腊北部葡萄园的多光谱无人机深度学习制图案例，方法新颖但属区域性研究，对国内智慧农业有参考价值。",[299],{"name":293,"url":290},[171,21,22,24,301],"葡萄园管理",{"code":44,"slug":112,"name":113,"path":114,"parent_code":115},[304],{"code":33,"slug":173,"name":174,"path":175,"parent_code":115},{"status":118,"locked":14,"version":28,"method":29,"evidence":306},{"candidates":307,"agricultural_evidence":35},[308,312,316,321,328],{"code":44,"score":164,"supported":35,"title":309,"abstract":310,"tags":311},[48],[24,124,48],[24],{"code":33,"score":165,"supported":35,"title":313,"abstract":314,"tags":315},[185],[184,185],[22],{"code":192,"score":45,"supported":35,"title":317,"abstract":318,"tags":320},[],[319,254],"精准农业",[171],{"code":322,"score":53,"supported":35,"title":323,"abstract":324,"tags":327},"B2",[],[325,326],"葡萄","grape",[],{"code":52,"score":53,"supported":35,"title":329,"abstract":330,"tags":332},[],[331,57],"空间分析",[],[334,335],"希腊 葡萄园 无人机 深度学习","多光谱 UAV 葡萄园 制图","希腊葡萄园无人机深度学习-4505","10.5281\u002Fzenodo.23232412",{"doi":337,"openalex_id":339,"authors":340,"venue":293,"cited_by_count":15,"oa_url":290,"card":354,"direction":83,"ingested_from":86},"W7220993218",[341,343,346,348,351],{"name":342,"orcid":9},"Dimitra Passa",{"name":344,"orcid":345},"Spyridon E. Detsikas","https:\u002F\u002Forcid.org\u002F0000-0002-3960-9361",{"name":347,"orcid":9},"Georgios Nektarios Tselos",{"name":349,"orcid":350},"George P. Petropoulos","https:\u002F\u002Forcid.org\u002F0000-0003-1442-1423",{"name":352,"orcid":353},"Antonis Litke","https:\u002F\u002Forcid.org\u002F0000-0002-7658-2559",{"tldr":355,"method":356,"finding":357,"direction":83,"opportunity":358},"用多光谱无人机影像和深度学习实现葡萄园制图与冠层特征提取。","多光谱无人机影像、RGB与多光谱融合、深度学习、ArcGIS Pro空间分析。","UAV+DL+GIS可灵活构建葡萄园精准管理的数据驱动框架。","可探索跨品种、跨生长阶段的迁移学习与轻量化模型，提升葡萄园实时监测泛化能力。","2026-10-09T23:30:25.912664Z",{"id":361,"title":362,"url":363,"summary":364,"summary_zh":365,"content":9,"source_name":366,"source_url":363,"published_at":367,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":368,"score_detail":369,"sources":371,"tags":373,"primary_topic":9,"secondary_topics":375,"classification":376,"search_phrases":393,"slug":396,"view_count":89,"doi":397,"paper":398,"created_at":410},4300,"Road surface material classification from unmanned aerial vehicle red, green, blue and multispectral imagery using spectral features and ensemble machine learning model","https:\u002F\u002Fdoi.org\u002F10.12913\u002F22998624\u002F226062","The aim of this study was to evaluate the impact of incorporating multispectral data acquired from an unmanned aerial vehicle (UAV) as an extension of standard RGB imagery on the accuracy of road surface material classification using machine learning algorithms.The research was conducted on six road sections differing in pavement type and technical condition.The performance of three ensemble learning algorithms: Random Forest, XGBoost, and LightGBM was investigated.Three input data configurations were considered: RGB imagery, a seven-band dataset comprising RGB channels and four multispectral bands (MS7), and an extended dataset (MS9) additionally including the NDVI and NDRE vegetation indices.For the best-performing model, the influence of texture features calculated within a moving 5 × 5 pixel window and edge information derived using the Sobel operator was also evaluated.The results demonstrated that the inclusion of multispectral data significantly improved classification performance compared with RGB imagery alone.The highest accuracy was achieved by the LightGBM model using the MS9 dataset, reaching an overall accuracy (OA) of 81.31%.The incorporation of texture features further increased the classification accuracy to 93.30% OA.The findings confirm the positive contribution of UAVacquired multispectral data to the effectiveness of road surface material classification.","本研究旨在评估将无人机（UAV）获取的多光谱数据作为标准RGB影像的扩展，对基于机器学习算法的路面材料分类精度的影响。研究在六段路面类型和技术状况不同的道路上进行，考察了三种集成学习算法——随机森林（Random Forest）、XGBoost和LightGBM的性能。研究考虑了三种输入数据配置：RGB影像、包含RGB通道和四个多光谱波段的七波段数据集（MS7），以及额外加入NDVI和NDRE植被指数的扩展数据集（MS9）。对于表现最佳的模型，还评估了在5×5像素移动窗口内计算的纹理特征以及利用Sobel算子提取的边缘信息的影响。结果表明，与仅使用RGB影像相比，加入多光谱数据显著提升了分类性能。使用MS9数据集的LightGBM模型取得了最高精度，总体精度（OA）达到81.31%。引入纹理特征后，分类精度进一步提升至93.30% OA。研究结果证实了无人机获取的多光谱数据对路面材料分类效果具有积极贡献。","Advances in Science and Technology – Research Journal","2026-10-04T00:00:00Z",64,{"impact":101,"substance":102,"depth":235,"authority":166,"freshness":101,"relevant":89,"comment":370},"该研究利用无人机多光谱影像与集成机器学习提升路面材料分类精度，方法新颖、数据详实，对农业遥感与智慧农业有参考价值，但属细分领域论文，公共影响有限。",[372],{"name":366,"url":363},[171,21,22,24,374],"路面分类",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":377},{"candidates":378,"agricultural_evidence":14,"reason":59},[379,385,389],{"code":33,"score":165,"supported":35,"title":380,"abstract":382,"tags":384},[381],"machine learning",[383,381],"机器学习",[22],{"code":44,"score":166,"supported":35,"title":386,"abstract":387,"tags":388},[48],[48],[24],{"code":192,"score":89,"supported":14,"title":390,"abstract":391,"tags":392},[],[],[171],[394,395],"无人机 多光谱 路面材料 分类","LightGBM 道路 遥感 分类","无人机多光谱路面材料分类-4300","10.12913\u002F22998624\u002F226062",{"doi":397,"openalex_id":399,"authors":400,"venue":366,"cited_by_count":15,"oa_url":404,"card":405,"direction":85,"ingested_from":86},"W7219783179",[401],{"name":402,"orcid":403},"Marcin Bilski","https:\u002F\u002Forcid.org\u002F0000-0002-1445-5749","https:\u002F\u002Fwww.astrj.com\u002Fpdf-226062-148948?filename=Road-surface-material-cla.pdf",{"tldr":406,"method":407,"finding":408,"direction":83,"opportunity":409},"用无人机RGB与多光谱影像结合集成机器学习，分类路面材料并验证多光谱与纹理特征的增益。","无人机RGB\u002F多光谱影像、NDVI\u002FNDRE、纹理与Sobel边缘，随机森林\u002FX","多光谱数据显著优于纯RGB，LightGBM用MS9达81.31%，加入纹理特征后提升至93.30%","可迁移到农田道路、机耕道与田间通行性识别，结合时序影像与轻量模型服务农机导航。","2026-10-06T23:30:09.562617Z",{"id":412,"title":413,"url":414,"summary":7,"summary_zh":415,"content":9,"source_name":416,"source_url":414,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":417,"sources":419,"tags":421,"primary_topic":9,"secondary_topics":423,"classification":424,"search_phrases":439,"slug":441,"view_count":442,"doi":443,"paper":444,"created_at":459},4218,"YOLO-BASED SYSTEMS FOR DETECTING EXPLOSIVE REMNANTS OF WAR: A REVIEW FOR HUMANITARIAN DEMINING","https:\u002F\u002Fdoi.org\u002F10.20998\u002F2522-9052.2026.4.10","相关性。人道主义排雷仍然是一项关键的安全挑战，因为战争遗留爆炸物、地雷和未爆弹药继续危及平民并阻碍冲突后恢复。在此背景下，人工智能和计算机视觉越来越多地被用于支持基于无人机（UAV）的勘察作业中的危险物体检测。本文旨在综合当前对地雷、未爆弹药和其他战争遗留爆炸物的检测方法，并明确YOLO模型在危险区域勘察集成技术流程中的作用。综述的对象是利用无人机、传感器系统、地理信息技术和YOLO系列模型检测危险物体的技术解决方案。论文的主题是基于YOLO的系统在集成流程中从视觉和光谱数据定位潜在爆炸物体的应用。研究结果。综述表明，现代方法结合了探地雷达、磁力测量、热传感、多光谱成像、高光谱成像、基于无人机的检查、GIS平台以及边缘\u002F云架构。YOLO在快速定位视觉或光谱上可表现出的危险物体指标方面展现出强大潜力，尤其是与其他传感模态结合使用时。结论。与此同时，YOLO不能作为宣布某一区域安全的唯一依据。其应用受到目标尺寸小、伪装、域偏移、标注数据集有限以及假阴性成本高的制约。因此，基于YOLO的检测应被视为多传感器、人工监督的决策支持系统中的一个组成部分。","Advanced Information Systems",{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":418},"主题为人道主义排雷中的YOLO目标检测综述，属军事安全与遥感交叉领域，与三农、农业信息化无直接关联，不建议进入每日精选。",[420],{"name":416,"url":414},[21,422,23,24],"人工智能",[],{"status":27,"locked":14,"version":28,"method":29,"evidence":425},{"candidates":426,"agricultural_evidence":14,"reason":59},[427,431,435],{"code":33,"score":34,"supported":35,"title":428,"abstract":429,"tags":430},[37],[39,40,41,37],[422,23],{"code":44,"score":45,"supported":35,"title":432,"abstract":433,"tags":434},[],[48,49],[24],{"code":52,"score":53,"supported":35,"title":436,"abstract":437,"tags":438},[],[56,57],[],[61,440],"未爆弹药 遥感 检测","YOLO排雷无人机-4218",2,"10.20998\u002F2522-9052.2026.4.10",{"doi":443,"openalex_id":445,"authors":446,"venue":416,"cited_by_count":15,"oa_url":453,"card":454,"direction":85,"ingested_from":86},"W7216173401",[447,448,450,451,452],{"name":68,"orcid":9},{"name":449,"orcid":9},"Denys Levchenko",{"name":73,"orcid":9},{"name":75,"orcid":76},{"name":78,"orcid":9},"https:\u002F\u002Fais.khpi.edu.ua\u002Farticle\u002Fdownload\u002F372728\u002F356290",{"tldr":455,"method":456,"finding":457,"direction":83,"opportunity":458},"综述YOLO模型在无人机人道排雷中检测地雷与未爆弹药的技术管线。","综述探地雷达、磁力、热成像、多\u002F高光谱、无人机与GIS结合YOLO的方法。","YOLO可快速定位危险目标，但受小目标、伪装、域偏移等限制，不能单独判定区域安全。","可将多传感器+YOLO的排雷管线迁移到农田未爆物检测与农业遥感安全评估中。","2026-10-05T23:30:11.995589Z"]