[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4800":3,"related-4800":61},{"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":42,"slug":43,"view_count":44,"doi":45,"paper":46,"created_at":60},4800,"A Survey of Object Detection and Classification in Video Using Deep Learning Approaches","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijcsmt.vol.11.no9.2025.pg22.38","Object detection and classification in videos has emerged as a core challenge in computer vision, with applications spanning autonomous driving, surveillance, healthcare, robotics, and human– computer interaction. Unlike still-image object detection, video-based detection requires handling additional complexities such as temporal coherence, motion blur, occlusion, and real-time constraints. Traditional approaches relying on handcrafted features and background subtraction have given way to deep learning-based methods, particularly convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and one-stage detectors such as the You Only Look Once (YOLO) family. This paper provides a comprehensive review of object detection in videos, surveying the evolution of methods from early handcrafted approaches to recent deep architectures. We examine popular models, datasets, and benchmarks, with an emphasis on accuracy–speed trade-offs and the unique challenges of real-world deployment. A comparative analysis of state-of-the-art methods highlights trends and gaps, including the struggle with small object detection, domain adaptation, and efficient training on large-scale datasets. Finally, we outline promising future directions such as transformer-based detection, self-supervised learning, multimodal video understanding, and edge-device optimization. This survey aims to serve as both an introductory resource and a roadmap for advancing video-based object detection research.","视频中的目标检测与分类已成为计算机视觉领域的核心挑战，其应用涵盖自动驾驶、监控、医疗健康、机器人技术及人机交互等多个方向。与静态图像目标检测不同，基于视频的检测需要处理额外的复杂性，如时间一致性、运动模糊、遮挡以及实时性约束。依赖手工设计特征和背景减除的传统方法已让位于基于深度学习的方法，尤其是卷积神经网络（CNN）、循环神经网络（RNN）、Transformer以及单阶段检测器如You Only Look Once（YOLO）系列。本文对视频中的目标检测进行了全面综述，梳理了从早期手工设计方法到近期深度架构的方法演进。我们考察了主流模型、数据集和基准测试，重点关注精度与速度之间的权衡以及真实世界部署中的独特挑战。对当前最先进方法的比较分析揭示了相关趋势与不足，包括在小目标检测、域适应以及大规模数据集高效训练方面的困境。最后，我们展望了有前景的未来方向，如基于Transformer的检测、自监督学习、多模态视频理解以及边缘设备优化。本综述旨在为推进基于视频的目标检测研究提供入门资源和路线图。",null,"INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY E-ISSN","2026-10-12T00:00:00Z","论文",10,false,0,{"impact":15,"substance":15,"depth":15,"authority":15,"freshness":15,"relevant":15,"comment":17},"该文为计算机视觉领域视频目标检测综述，与三农、农业信息化、智慧农业等主题无关，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":41},[28],{"code":29,"score":30,"supported":31,"title":32,"abstract":34,"tags":40},"I2",17,true,[33],"deep learning",[35,36,37,33,38,39],"目标检测","深度学习","计算机视觉","computer vision","YOLO",[],"农业相关性需要复核",[],"ASurveyofObjectDetectionandClassificatio-4800",1,"10.56201\u002Fijcsmt.vol.11.no9.2025.pg22.38",{"doi":45,"openalex_id":47,"authors":48,"venue":10,"cited_by_count":15,"oa_url":51,"card":52,"direction":58,"ingested_from":59},"W7221521061",[49],{"name":50,"orcid":9},"Umar BELLO","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FIJCSMT\u002FVOL. 11 NO. 9 2025\u002FA Survey of Object Detection 22-38.pdf",{"tldr":53,"method":54,"finding":55,"direction":56,"opportunity":57},"综述视频目标检测与分类从手工特征到深度学习方法的演进、模型、数据集与挑战。","综述CNN、RNN、Transformer及YOLO等单阶段检测器，比较精度-速","小目标检测、域适应和大规模高效训练仍是主要瓶颈，Transformer与自监督是趋势。","农业人工智能与决策模型","可将视频目标检测迁移至农业场景，如畜禽行为识别、果实计数与农机作业监测。","智慧农业 \u002F 农业物联网","openalex","2026-10-13T23:30:08.611538Z",{"total":15,"page":44,"page_size":44,"items":62},[]]