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.
DownLoad: