宋怀波, 王云飞, 邓洪兴, 等. 基于视频分析的规模化奶牛智能监测技术研究进展[J]. 华南农业大学学报, 2024, 45(5): 1-12. doi: 10.7671/j.issn.1001-411X.202403038
    引用本文: 宋怀波, 王云飞, 邓洪兴, 等. 基于视频分析的规模化奶牛智能监测技术研究进展[J]. 华南农业大学学报, 2024, 45(5): 1-12. doi: 10.7671/j.issn.1001-411X.202403038
    SONG Huaibo, WANG Yunfei, DENG Hongxing, et al. Research progress of intelligent monitoring technology for large-scale dairy cows based on video analysis[J]. Journal of South China Agricultural University, 2024, 45(5): 1-12. doi: 10.7671/j.issn.1001-411X.202403038
    Citation: SONG Huaibo, WANG Yunfei, DENG Hongxing, et al. Research progress of intelligent monitoring technology for large-scale dairy cows based on video analysis[J]. Journal of South China Agricultural University, 2024, 45(5): 1-12. doi: 10.7671/j.issn.1001-411X.202403038

    基于视频分析的规模化奶牛智能监测技术研究进展

    Research progress of intelligent monitoring technology for large-scale dairy cows based on video analysis

    • 摘要: 奶牛智能监测是规模化奶牛养殖的重要环节,视频分析技术具备无接触、低成本及智能分析优势,已成为当前规模化奶牛智能监测技术研究的热点。奶牛目标检测、目标跟踪以及个体和行为识别技术对规模化奶牛监管具有重要意义,复杂养殖环境中的光照、昼夜交替变化、围栏遮挡以及牛群数量繁多导致的相互遮挡是影响规模化奶牛智能监测的重要因素。本文对基于视频分析的奶牛智能监测技术研究中常用的深度模型及应用情况进行综述,提出了当前研究中面临的问题与挑战。分析发现,注意力机制、混合卷积等技术是提高模型识别准确率的有效方法,轻量化模块有利于减少模型的复杂度与计算量;计算复杂度、普适性、准确性等是影响该技术推广实用的因素;具体应用时,需要针对奶牛养殖环境、奶牛状况等进行具体分析以不断满足规模化养殖的需求。

       

      Abstract: Cow intelligent monitoring is an important link in large-scale dairy farming. Video analysis has the advantages of contactless, low-cost, and intelligent analysis, and has become a hot spot in the research of intelligent identification technology of large-scale dairy cows. Dairy cow target detection, target tracking, and the technologies of individual and behavior recognition are of great significance for large-scale dairy cow supervision. Lighting, day and night alternations, fence occlusion and mutual occlusion caused by large number of cows in complex breeding environment are serious factors affecting the intelligent monitoring of large-scale dairy cows. This paper summarized the depth models and practical application commonly used in cow intelligent monitoring. The problems and challenges faced in the current research were put forward. The analysis result showed that the attention mechanism and hybrid convolution were effective methods to improve the recognition accuracy of the model, and the lightweight modules were conducive to reducing the complexity and computation of the model. The factors that affected the current research to be practical were computational complexity, universality and accuracy. It is necessary to conduct specific analyses based on the dairy farming environment and the condition of dairy cows to continuously meet the needs of large-scale farming while applying this technology.

       

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