[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3905":3,"related-3905":110},{"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":24,"tags":26,"primary_topic":9,"secondary_topics":32,"classification":33,"search_phrases":61,"slug":64,"view_count":65,"doi":66,"paper":67,"created_at":109},3905,"Deep learning-based monitoring and spatiotemporal analysis of wildlife and human activities using camera trap imagery","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10344-026-02156-x","Abstract Wildlife and human activities are key components of ecological systems, and understanding their spatiotemporal patterns is essential for evaluating human–wildlife interactions and informing conservation planning. Camera trap networks function as terrestrial remote sensing systems that enable continuous monitoring of ecological processes at fine spatiotemporal scales. In this study, camera trap imagery collected in Chitwan National Park (CNP), Nepal, and its surrounding zones, under visible light during daytime and infrared illumination at night, was organized into a multi-class dataset containing wildlife, humans, and domestic animals. To support the ecological interpretation of wildlife–human spatiotemporal patterns, deep learning–based object detection models (YOLOv5 and YOLOv11) were trained to extract animal and human occurrences from the imagery. The resulting detections were integrated into a spatiotemporal analysis framework to characterize wildlife and human activity patterns, map spatial co-occurrence across the landscape, and quantify temporal proximity between human-wildlife pairs. Among the evaluated models, YOLOv11s achieved the best detection performance, with a precision of 95.5%, recall of 92.9%, and mean Average Precision50 (mAP50) of 95.4%, indicating reliable performance in real-world scenarios. Spatiotemporal analysis revealed shorter temporal lags for human–rhino pairs compared to human–tiger pairs, and identified a clear spatial gradient of human–wildlife co-occurrence, with the highest intensity in the Buffer Zone, followed by the Corridor Forest and the CNP. These results demonstrate that integrating automated detection with spatiotemporal analysis transforms camera trap data into ecological information for scalable monitoring. The proposed framework identifies areas of elevated human–wildlife spatiotemporal proximity, helping prioritize targeted monitoring and landscape management.","野生动物与人类活动是生态系统的关键组成部分，理解其时空格局对于评估人–野生动物相互作用和指导保护规划至关重要。相机陷阱网络作为陆地遥感系统，能够在精细的时空尺度上持续监测生态过程。本研究将尼泊尔奇旺国家公园（Chitwan National Park, CNP）及其周边区域在白天可见光和夜间红外照明条件下采集的相机陷阱影像整理为一个包含野生动物、人类和家养动物的多类别数据集。为支持野生动物–人类时空格局的生态学解读，本研究训练了基于深度学习的物体检测模型（YOLOv5和YOLOv11），以从影像中提取动物和人类出现记录。所得检测结果被整合到一个时空分析框架中，用以刻画野生动物和人类活动格局、绘制景观尺度上的空间共现分布，并量化人–野生动物配对之间的时间邻近性。在评估的模型中，YOLOv11s取得了最佳检测性能，精确率为95.5%，召回率为92.9%，平均精度均值50（mAP50）为95.4%，表明其在实际场景中具有可靠性能。时空分析揭示，人–犀牛配对的时间滞后短于人–虎配对，并识别出人–野生动物共现的明显空间梯度，其中缓冲区强度最高，其次为廊道林和CNP。这些结果表明，将自动检测与时空分析相结合，可将相机陷阱数据转化为用于可扩展监测的生态信息。所提出的框架识别了人–野生动物时空邻近性较高的区域，有助于优先开展针对性监测和景观管理。",null,"European Journal of Wildlife Research","2026-09-29T00:00:00Z","论文",10,false,75,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,21,18,13,8,1,"该研究将YOLOv5\u002FYOLOv11深度学习模型应用于尼泊尔奇旺国家公园红外相机影像，实现野生动物与人类活动的高精度检测及时空共现分析，方法新颖、数据可靠，对农业信息化中的生态监测与遥感应用有参考价值，但属国际案例、非国内三农政策或产业事件，故未达每日精选门槛。",[25],{"name":10,"url":6},[27,28,29,30,31],"深度学习","目标检测","生态保护","野生动物监测","红外相机",[],{"status":34,"locked":14,"version":35,"method":36,"evidence":37},"needs_review","2026-10-22.2","rules",{"candidates":38,"agricultural_evidence":14,"reason":60},[39,47,55],{"code":40,"score":41,"supported":42,"title":43,"abstract":45,"tags":46},"I2",16,true,[44],"deep learning",[27,44],[27,28],{"code":48,"score":49,"supported":42,"title":50,"abstract":51,"tags":54},"I3",6,[],[52,53],"遥感","remote sensing",[],{"code":56,"score":22,"supported":14,"title":57,"abstract":58,"tags":59},"H6",[],[],[29],"农业相关性需要复核",[62,63],"野生动物监测 深度学习 生态保护 目标检测","野生动物监测 深度学习","野生动物监测深度学习生态保护目标检测-3905",0,"10.1007\u002Fs10344-026-02156-x",{"doi":66,"openalex_id":68,"authors":69,"venue":10,"cited_by_count":65,"oa_url":100,"card":101,"direction":107,"ingested_from":108},"W7214779795",[70,72,75,77,79,82,85,88,91,94,97],{"name":71,"orcid":9},"Hao Chen",{"name":73,"orcid":74},"Fang Qiu","https:\u002F\u002Forcid.org\u002F0000-0003-3999-1174",{"name":76,"orcid":9},"Umme Kulsum",{"name":78,"orcid":9},"Caiyun Zhang",{"name":80,"orcid":81},"Eve Bohnett","https:\u002F\u002Forcid.org\u002F0000-0002-1870-8897",{"name":83,"orcid":84},"Haitao Lyu","https:\u002F\u002Forcid.org\u002F0000-0001-7710-3284",{"name":86,"orcid":87},"Shuang Tian","https:\u002F\u002Forcid.org\u002F0000-0001-7730-5193",{"name":89,"orcid":90},"Babu Ram Lamichhane","https:\u002F\u002Forcid.org\u002F0000-0003-0580-7682",{"name":92,"orcid":93},"Douglas Alan Stow","https:\u002F\u002Forcid.org\u002F0000-0001-5246-7073",{"name":95,"orcid":96},"Rebecca L. Lewison","https:\u002F\u002Forcid.org\u002F0000-0003-3065-2926",{"name":98,"orcid":99},"Li An","https:\u002F\u002Forcid.org\u002F0000-0002-7933-5174","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs10344-026-02156-x.pdf",{"tldr":102,"method":103,"finding":104,"direction":105,"opportunity":106},"用YOLO模型从红外相机图像中检测野生动物与人类，并分析其时空共现格局。","YOLOv5\u002FYOLOv11检测尼泊尔奇旺国家公园相机陷阱图像，结合时空分析框架","YOLOv11s检测最优（mAP50 95.4%），人-犀牛时间间隔短于人-虎，缓冲区共现强度最高。","农业人工智能与决策模型","可将该检测-时空分析框架迁移至农田野生动物监测，评估作物损害风险并优化防护决策。","农业遥感与作物表型","openalex","2026-10-01T23:30:30.498000Z",{"total":49,"page":22,"page_size":49,"items":111},[112,184,234,283,340,397],{"id":113,"title":114,"url":115,"summary":116,"summary_zh":117,"content":9,"source_name":118,"source_url":115,"published_at":119,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":120,"score_detail":121,"sources":124,"tags":126,"primary_topic":130,"secondary_topics":135,"classification":136,"search_phrases":156,"slug":159,"view_count":160,"doi":161,"paper":162,"created_at":183},1310,"Object Detection in Computer Vision: A Comprehensive Review of Algorithms, Datasets, and Evaluation Techniques","https:\u002F\u002Fdoi.org\u002F10.20944\u002Fpreprints202608.2288.v1","Object detection has evolved dramatically over three decades, transitioning from handcrafted features to deep learning, transformers, and now large language models. This paper provides a comprehensive systematic review of this evolution, covering traditional methods (SIFT, HOG, Viola-Jones), CNN-based single- and two-stage detectors (R-CNN family, YOLO v1–v12, SSD), transformer-based architectures (DETR, ViT, Swin), and emerging LLM-driven approaches (GPT-4V, Grounding DINO, YOLO-World). We benchmark and compare these methods using key metrics—mAP, IoU, FPS—and analyze over 50 datasets across generic, autonomous driving, medical, remote sensing, and agricultural domains. Our critical synthesis identifies persistent research gaps: lightweight edge deployment, sensor fusion for real-time systems, bias and fairness in AI, and multimodal vision-language integration. By consolidating three decades of progress and outlining future challenges, this survey serves as a valuable resource for researchers and practitioners developing accurate, efficient, and reliable object detection systems.","目标检测在过去三十年间经历了深刻的演变，从手工特征工程发展到深度学习、Transformer架构，直至如今的大语言模型。本文对这一演进过程进行了全面的系统性综述，涵盖了传统方法（SIFT、HOG、Viola-Jones）、基于CNN的单阶段和双阶段检测器（R-CNN系列、YOLO v1–v12、SSD）、基于Transformer的架构（DETR、ViT、Swin），以及新兴的由大语言模型驱动的方法（GPT-4V、Grounding DINO、YOLO-World）。我们使用关键指标——mAP、IoU、FPS——对这些方法进行了基准测试和比较，并分析了涵盖通用、自动驾驶、医学、遥感及农业领域的50多个数据集。我们的批判性综合指出了持续存在的研究空白：轻量级边缘部署、用于实时系统的传感器融合、人工智能中的偏见与公平性，以及多模态视觉-语言集成。通过整合三十年的进展并勾勒未来挑战，本综述为开发准确、高效且可靠的目标检测系统的研究人员和从业者提供了宝贵的资源。","Preprints.org","2026-08-31T00:00:00Z",56,{"impact":21,"substance":19,"depth":19,"authority":13,"freshness":122,"relevant":22,"comment":123},2,"综述覆盖农业领域目标检测应用，但时效性低，影响有限。",[125],{"name":118,"url":115},[127,128,129,27,28],"智慧农业","农业人工智能","综述",{"code":40,"slug":131,"name":132,"path":133,"parent_code":134},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":137,"locked":14,"version":35,"method":36,"evidence":138},"classified",{"candidates":139,"agricultural_evidence":42},[140,147,151],{"code":40,"score":141,"supported":42,"title":142,"abstract":144,"tags":146},20,[143],"computer vision",[28,27,44,145],"YOLO",[128,27,28],{"code":48,"score":49,"supported":42,"title":148,"abstract":149,"tags":150},[],[52,53],[],{"code":152,"score":22,"supported":14,"title":153,"abstract":154,"tags":155},"I1",[],[],[127],[157,158],"农业人工智能 智慧农业 深度学习 目标检测","农业人工智能 智慧农业","农业人工智能智慧农业深度学习目标检测-1310",3,"10.20944\u002Fpreprints202608.2288.v1",{"doi":161,"openalex_id":163,"authors":164,"venue":118,"cited_by_count":65,"oa_url":115,"card":178,"direction":107,"ingested_from":108},"W7204770977",[165,168,170,172,174,176],{"name":166,"orcid":167},"Md. Faishal Rahaman","https:\u002F\u002Forcid.org\u002F0000-0001-5656-1542",{"name":169,"orcid":9},"Li Xueyuan",{"name":171,"orcid":9},"Md Nagib Mahfuz Sunny",{"name":173,"orcid":9},"Muhammad Amjad",{"name":175,"orcid":9},"Xin Gao",{"name":177,"orcid":9},"Sakib Hasan",{"tldr":179,"method":180,"finding":181,"direction":105,"opportunity":182},"系统综述目标检测三十年发展，从传统方法到深度学习、Transformer及大语言模型，并分析数据集与","系统综述，涵盖传统、CNN、Transformer及LLM方法，比较mAP、Io","识别出轻量边缘部署、传感器融合、公平性及多模态视觉语言集成等研究空白。","农业领域目标检测可探索轻量模型与多模态融合，结合遥感数据提升复杂场景下的实时检测精度。","2026-09-01T23:30:19.994895Z",{"id":185,"title":186,"url":187,"summary":188,"summary_zh":9,"content":9,"source_name":189,"source_url":9,"published_at":190,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":191,"score_detail":192,"sources":197,"tags":199,"primary_topic":202,"secondary_topics":203,"classification":204,"search_phrases":229,"slug":232,"view_count":13,"doi":9,"paper":9,"created_at":233},465,"A Comparative Evaluation of Deep Learning Object Detection Models on a Real-World Multi-Plant Dataset from Africa","https:\u002F\u002Farxiv.org\u002Fabs\u002F2608.11053","本文对YOLOv5、YOLOv8、YOLO11、YOLO26、Faster R-CNN、RT-DETR六种目标检测模型在尼日利亚农场实地采集的真实世界数据集AgriAISeg 1(3382张芝麻、卷心菜、番茄图像)上进行比较,涵盖不同光照、遮挡和视角条件。结果显示RT-DETR整体表现最优,精度0.768、mAP@0.5:0.95达0.624;YOLOv8和YOLO11表现稳定强劲;Faster R-CNN精度显著较低,mAP@0.5仅0.466,YOLO系模型训练效率优于Faster R-CNN。研究表明,现代单阶段和基于Transformer的检测器在真实世界农业环境中提供更可靠、高效的植物检测解决方案。原文标题:A Comparative Evaluation of Deep Learning Object Detection Models on a Real-World Multi-Plant Dataset from Africa,作者:Ismail Ismail Tijjani等8人,arXiv:2608.11053,2026年8月11日提交。","arXiv 2026-08-11 [预印本]","2026-08-11T00:00:00Z",71,{"impact":193,"substance":194,"depth":19,"authority":13,"freshness":195,"relevant":22,"comment":196},12,22,9,"针对非洲真实多作物数据集的检测模型对比，数据详实，结论对农业AI应用有参考价值。",[198],{"name":189,"url":187},[27,28,200,201],"农作物识别","非洲农业",{"code":40,"slug":131,"name":132,"path":133,"parent_code":134},[],{"status":137,"locked":14,"version":35,"method":36,"evidence":205},{"candidates":206,"agricultural_evidence":42},[207,212,218,224],{"code":40,"score":208,"supported":42,"title":209,"abstract":210,"tags":211},19,[44],[28,44,145],[27,28],{"code":213,"score":160,"supported":42,"title":214,"abstract":215,"tags":217},"A4",[],[216],"芝麻",[],{"code":219,"score":160,"supported":42,"title":220,"abstract":221,"tags":223},"B1",[],[222],"番茄",[],{"code":225,"score":22,"supported":14,"title":226,"abstract":227,"tags":228},"J3",[],[],[201],[230,231],"农作物识别 深度学习 目标检测 非洲农业","农作物识别 深度学习","农作物识别深度学习目标检测非洲农业-465","2026-08-12T23:57:24.252543Z",{"id":235,"title":236,"url":237,"summary":238,"summary_zh":9,"content":9,"source_name":239,"source_url":9,"published_at":240,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":241,"sources":244,"tags":246,"primary_topic":250,"secondary_topics":256,"classification":258,"search_phrases":269,"slug":272,"view_count":65,"doi":9,"paper":273,"created_at":282},5534,"DC-YOLO：应对遮挡与光照变化的湖羊行为检测模型","https:\u002F\u002Ffinance.sina.com.cn\u002Froll\u002F2026-10-19\u002Fdoc-inivumaf7730137.shtml","中国农业大学团队针对集约化养殖中多目标严重遮挡、光照多变与类别不均问题，引入自适应细节—上下文注意力机制构建模块，并采用边界框相似度软抑制防止误删遮挡目标，预训练光照编码器迁移参数应对光照变化。数据集来自湖羊养殖场全天候监控，含505张标注图像、5类行为。","智慧农业（中英文）2026-10-19","2026-10-19T00:00:00Z",{"impact":19,"substance":18,"depth":242,"authority":20,"freshness":49,"relevant":22,"comment":243},17,"论文提出面向湖羊行为检测的DC-YOLO模型，方法有新意、数据来自真实养殖场，属智慧养殖细分领域实质进展，值得精选。",[245],{"name":239,"url":237},[128,247,28,248,249],"智慧养殖","湖羊","动物行为检测",{"code":251,"slug":252,"name":253,"path":254,"parent_code":255},"C2","ruminants","草食牲畜","\u002Ftopic\u002Flivestock\u002Fruminants","C",[257],{"code":40,"slug":131,"name":132,"path":133,"parent_code":134},{"status":137,"locked":14,"version":35,"method":36,"evidence":259},{"candidates":260,"agricultural_evidence":42},[261,265],{"code":251,"score":193,"supported":42,"title":262,"abstract":263,"tags":264},[248],[248],[248],{"code":40,"score":13,"supported":42,"title":266,"abstract":267,"tags":268},[145],[],[128,28],[270,271],"中国农业大学 湖羊 行为检测","DC-YOLO 遮挡 光照","中国农业大学湖羊行为检测-5534",{"doi":9,"openalex_id":9,"authors":274,"venue":9,"cited_by_count":65,"oa_url":9,"card":275,"direction":279,"ingested_from":281},[],{"tldr":276,"method":277,"finding":278,"direction":279,"opportunity":280},"提出DC-YOLO模型，用于湖羊行为检测，解决遮挡与光照变化问题。","自适应细节-上下文注意力、边界框相似度软抑制、光照编码器迁移，505张标注图像。","模型在遮挡和光照变化下有效检测湖羊行为，减少误删。","智慧农业 \u002F 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