Black cattle body shape and temperature measurement using thermography and KINECT sensor

Kawasue, Kikuhito; Win, Khin Dagon; Yoshida, Kumiko; Tokunaga, Tadaaki

doi:10.1007/s10015-017-0373-2

Cited by 28 publications

(10 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the fact the 3D PCD is influenced by geometry of target cattle, the view scope of 3D camera and continuous movement of live cattle, it is necessary to stitch and register the different and scattered surface data into a whole and high-quality contour. The Iterative Closest Point (ICP) fast stitching and registration method [ 57 , 58 ] is utilized for the accurate registration. For an accurate and reliable method for registration of free form surfaces, the ICP algorithm is used for finding the rigid transformation iteratively between the target point set and the source point set so that two matching Euclidean distance satisfy the optimal match at a given convergence threshold.…”

Section: Methodsmentioning

confidence: 99%

Non-Contact Body Measurement for Qinchuan Cattle with LiDAR Sensor

Huang

Zhu

et al. 2018

Sensors

View full text Add to dashboard Cite

The body dimension measurement of large animals plays a significant role in quality improvement and genetic breeding, and the non-contact measurements by computer vision-based remote sensing could represent great progress in the case of dangerous stress responses and time-costing manual measurements. This paper presents a novel approach for three-dimensional digital modeling of live adult Qinchuan cattle for body size measurement. On the basis of capturing the original point data series of live cattle by a Light Detection and Ranging (LiDAR) sensor, the conditional, statistical outliers and voxel grid filtering methods are fused to cancel the background and outliers. After the segmentation of K-means clustering extraction and the RANdom SAmple Consensus (RANSAC) algorithm, the Fast Point Feature Histogram (FPFH) is put forward to get the cattle data automatically. The cattle surface is reconstructed to get the 3D cattle model using fast Iterative Closest Point (ICP) matching with Bi-directional Random K-D Trees and a Greedy Projection Triangulation (GPT) reconstruction method by which the feature points of cattle silhouettes could be clicked and calculated. Finally, the five body parameters (withers height, chest depth, back height, body length, and waist height) are measured in the field and verified within an accuracy of 2 mm and an error close to 2%. The experimental results show that this approach could be considered as a new feasible method towards the non-contact body measurement for large physique livestock.

show abstract

Section: Methodsmentioning

confidence: 99%

Non-Contact Body Measurement for Qinchuan Cattle with LiDAR Sensor

Huang

Zhu

et al. 2018

Sensors

View full text Add to dashboard Cite

show abstract

“…To generate a 3D cattle body model based on depth image, the point cloud data of cattle can be collected by a Kinect sensor to obtain 3D information of the cattle body to quantify the evaluation of cattle body condition [15]. By using the advantages of 3D depth image, researchers combined a thermal imaging camera and proposed a system for measuring the body shape and temperature of black cattle to complete the periodic quality assessment during the growth of cattle [16]. Some researchers have used a depth camera to obtain the body dimension data of pigs to estimate their weight, and found that the average absolute error of the nonlinear model measured by non-contact depth camera was 40% lower than that of the same nonlinear model measured by hand [17].…”

Section: Introductionmentioning

confidence: 99%

Body Dimension Measurements of Qinchuan Cattle with Transfer Learning from LiDAR Sensing

Huang

Guo

Rao

et al. 2019

Sensors

View full text Add to dashboard Cite

For the time-consuming and stressful body measuring task of Qinchuan cattle and farmers, the demand for the automatic measurement of body dimensions has become more and more urgent. It is necessary to explore automatic measurements with deep learning to improve breeding efficiency and promote the development of industry. In this paper, a novel approach to measuring the body dimensions of live Qinchuan cattle with on transfer learning is proposed. Deep learning of the Kd-network was trained with classical three-dimensional (3D) point cloud datasets (PCD) of the ShapeNet datasets. After a series of processes of PCD sensed by the light detection and ranging (LiDAR) sensor, the cattle silhouettes could be extracted, which after augmentation could be applied as an input layer to the Kd-network. With the output of a convolutional layer of the trained deep model, the output layer of the deep model could be applied to pre-train the full connection network. The TrAdaBoost algorithm was employed to transfer the pre-trained convolutional layer and full connection of the deep model. To classify and recognize the PCD of the cattle silhouette, the average accuracy rate after training with transfer learning could reach up to 93.6%. On the basis of silhouette extraction, the candidate region of the feature surface shape could be extracted with mean curvature and Gaussian curvature. After the computation of the FPFH (fast point feature histogram) of the surface shape, the center of the feature surface could be recognized and the body dimensions of the cattle could finally be calculated. The experimental results showed that the comprehensive error of body dimensions was close to 2%, which could provide a feasible approach to the non-contact observations of the bodies of large physique livestock without any human intervention.

show abstract

“…Owing to the characteristics of non-contact, rapid detection, non-destructive, infrared thermal image technology was used to obtain the temperature of pig-body [5][6] [7]. To prove the characteristics of infrared images, a method of measuring pig head temperature was proposed based on infrared images [8].…”

Section: Pig-body Temperature Detectionmentioning

confidence: 99%