吕恩利, 曾伯阳, 曾志雄, 等. 基于点云边缘平滑和生物特征的鸡只体尺估测[J]. 华南农业大学学报, 2023, 44(4): 619-627. DOI: 10.7671/j.issn.1001-411X.202206041
    引用本文: 吕恩利, 曾伯阳, 曾志雄, 等. 基于点云边缘平滑和生物特征的鸡只体尺估测[J]. 华南农业大学学报, 2023, 44(4): 619-627. DOI: 10.7671/j.issn.1001-411X.202206041
    LÜ Enli, ZENG Boyang, ZENG Zhixiong, et al. Estimation of chicken body size based on point cloud edge smoothing and biometric features[J]. Journal of South China Agricultural University, 2023, 44(4): 619-627. DOI: 10.7671/j.issn.1001-411X.202206041
    Citation: LÜ Enli, ZENG Boyang, ZENG Zhixiong, et al. Estimation of chicken body size based on point cloud edge smoothing and biometric features[J]. Journal of South China Agricultural University, 2023, 44(4): 619-627. DOI: 10.7671/j.issn.1001-411X.202206041

    基于点云边缘平滑和生物特征的鸡只体尺估测

    Estimation of chicken body size based on point cloud edge smoothing and biometric features

    • 摘要:
      目的 针对使用深度相机的鸡只体尺估测中,鸡只点云边缘抖动、羽毛冗余、特征点提取难的问题,本文提出一种结合点云边缘平滑和基于生物特征的特征点提取方法用于鸡只多部位体尺估测。
      方法 首先,通过直通滤波、统计滤波等方法对点云进行预处理,减少背景和噪点对目标的影响;其次,通过点云的空间变化约束边缘,采用连续多帧序列变化平滑边缘,减少边缘抖动对体尺测点提取的干扰;再次,对处理后的点云进行生物特征分析,结合基于邻域分析的边缘算法,融合RGB图像采用Canny边缘检测、霍夫变换等方法提取特征点;最后,依据特征点估测胸宽、半潜水长和胫长体尺。
      结果 试验结果表明,估测的胸宽平均误差为6.64%,胫长平均误差为5.93%,半潜水长平均误差为3.34%,平均每帧图像计算体尺耗时8.8 s。
      结论 本文算法可为鸡只体尺测量提供技术参考。

       

      Abstract:
      Objective To address the issues of edge jitter in chicken point clouds, feather redundancy and challenging feature point extraction in chicken body size estimation using depth cameras, this paper proposes a method combining point cloud edge smoothing and biometric-based feature point extraction for mult-position estimation of chicken body size.
      Method Firstly, the point cloud was preprocessed by direct filtering, statistical filtering and other methods to reduce the impact of background and noise on the target. Secondly, the edge was constrained by the spatial change of point cloud, and the edge was smoothed by continuous multi-frame sequence changes, so as to reduce the interference of edge jitter on the extraction of body measurement points. Thirdly, the biological characteristics of the processed point cloud were analyzed. Combined with the edge algorithm based on neighborhood analysis, the RGB image was fused and the feature points were extracted by Canny edge detection, Hough transform and other methods. Finally, the chest width, semi diving length and tibial length were estimated according to the feature points.
      Result The test results showed that the average error of estimated chest width was 6.64%, the average error of tibial length was 5.93%, and the average error of semi diving length was 3.34%. The average calculation time of body size per frame image was 8.8 s.
      Conclusion The algorithm of this paper can provide a technical reference for chicken body size measurement.

       

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