[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4813":3,"related-4813":85},{"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":21,"classification":22,"search_phrases":50,"slug":51,"view_count":15,"doi":52,"paper":53,"created_at":84},4813,"AI ‐Powered Surveillance Systems: A Comprehensive Review of Technologies, Applications, Challenges, and Deployment Considerations","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fwidm.70137","ABSTRACT Artificial intelligence (AI) has rapidly transformed smart surveillance systems, significantly leveraging technologies such as deep learning, computer vision, and adaptive monitoring. Driven by the need for real‐time object detection, behavioral analysis, and automated alerting, AI‐powered surveillance is increasingly vital for enhancing public safety, operational efficiency, and decision‐making in fields such as security, health‐care, transportation, and environmental monitoring. The growing societal impact and complexity of surveillance necessitate a comprehensive examination of foundational methodologies, key application domains, and the technical, ethical, and operational challenges associated with these systems. Analysis reveals that although AI‐driven surveillance solutions offer improved accuracy, scalability, and responsiveness, they continue to face notable barriers including high computational demands, privacy and data quality concerns, interpretability limitations, and inadequate regulatory frameworks. The review further identifies critical emerging trends, such as sustainable AI practices, human‐AI collaborative models, and the integration of AI with the Internet of Things (IoT) and blockchain technologies, as significant influences shaping the future trajectory of the field. Addressing these challenges through ongoing research, innovation, and responsible deployment is essential to fully unlock the societal and industrial potential of AI‐enabled surveillance solutions.","人工智能（AI）已迅速改变了智能监控系统，显著利用了深度学习、计算机视觉和自适应监测等技术。在实时目标检测、行为分析和自动报警需求的驱动下，AI驱动的监控在安全、医疗、交通和环境监测等领域对于提升公共安全、运营效率和决策能力日益重要。监控日益增长的社会影响和复杂性，要求对其基础方法、关键应用领域以及相关技术、伦理和运营挑战进行全面审视。分析表明，尽管AI驱动的监控解决方案在准确性、可扩展性和响应能力方面有所提升，但仍面临显著障碍，包括高计算需求、隐私和数据质量问题、可解释性局限以及监管框架不完善。综述进一步指出了关键新兴趋势，如可持续AI实践、人机协同模型以及AI与物联网（IoT）和区块链技术的融合，这些趋势正在显著影响该领域的未来走向。通过持续研究、创新和负责任部署来应对这些挑战，对于充分释放AI赋能监控解决方案的社会和产业潜力至关重要。",null,"Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery","2026-10-11T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该文为AI监控系统综述，属公共安全与通用AI领域，与三农、农业信息化、智慧农业无直接关联，不予入选。",[19],{"name":10,"url":6},[12],[],{"status":23,"locked":14,"version":24,"method":25,"evidence":26},"needs_review","2026-10-22.2","rules",{"candidates":27,"agricultural_evidence":14,"reason":49},[28,41],{"code":29,"score":30,"supported":31,"title":32,"abstract":33,"tags":40},"I2",9,true,[],[34,35,36,37,38,39],"目标检测","深度学习","计算机视觉","deep learning","artificial intelligence","computer vision",[],{"code":42,"score":30,"supported":31,"title":43,"abstract":44,"tags":48},"I5",[],[45,46,47],"物联网","internet of things","IoT",[],"农业相关性需要复核",[],"AI‐PoweredSurveillanceSystems:AComprehen-4813","10.1002\u002Fwidm.70137",{"doi":52,"openalex_id":54,"authors":55,"venue":10,"cited_by_count":15,"oa_url":76,"card":77,"direction":81,"ingested_from":83},"W7221448651",[56,59,61,64,66,69,71,73],{"name":57,"orcid":58},"Tanzila Kehkashan","https:\u002F\u002Forcid.org\u002F0000-0002-6325-4409",{"name":60,"orcid":9},"Abdul Rehman",{"name":62,"orcid":63},"Ahmad Sami Al‐Shamayleh","https:\u002F\u002Forcid.org\u002F0000-0002-7222-2433",{"name":65,"orcid":9},"Nor Azman Ismail",{"name":67,"orcid":68},"Adnan Akhunzada","https:\u002F\u002Forcid.org\u002F0000-0001-8370-9290",{"name":70,"orcid":9},"Imran Ashraf",{"name":72,"orcid":9},"Nikola Ivković",{"name":74,"orcid":75},"Shahbaz Pervez","https:\u002F\u002Forcid.org\u002F0000-0003-0232-2356","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1002\u002Fwidm.70137",{"tldr":78,"method":79,"finding":80,"direction":81,"opportunity":82},"综述AI监控系统的技术、应用、挑战与部署，涵盖多领域并展望未来趋势。","文献综述，分析深度学习、计算机视觉、自适应监控及IoT\u002F区块链集成。","AI监控提升准确性与扩展性，但面临算力、隐私、可解释性和监管障碍。","智慧农业 \u002F 农业物联网","可将AI监控与IoT结合用于农业环境监测、畜禽行为分析，并探索边缘计算与隐私保护方案。","openalex","2026-10-13T23:30:10.814625Z",{"total":15,"page":86,"page_size":86,"items":87},1,[]]