Abstract
Sheep body segmentation robot can improve production hygiene, product quality, and cutting accuracy, which is a huge change for traditional manual segmentation. With reference to the New Zealand sheep body segmentation specification, a vision system for Cartesian coordinate robot cutting half-sheep was developed and tested. The workflow of the vision system was designed and the image acquisition device with an Azure Kinect sensor was developed. Furthermore, a LabVIEW software with the image processing algorithm was then integrated with the RGBD image acquisition device in order to construct an automatic vision system. Based on Deeplab v3+ networks, an image processing system for locating ribs and spine was employed. Taking advantage of the location characteristics of ribs and spine in the split half-sheep, a calculation method of cutting line based on the key points is designed to determine five cutting curves. The seven key points are located by convex points of ribs and spine and the root of hind leg. Using the conversion relation between depth image and the space coordinates, the 3D coordinates of the curves were computed. Finally, the kinematics equation of the rectangular coordinate robot arm is established, and the 3D coordinates of the curves are converted into the corresponding motion parameters of the robot arm. The experimental results indicated that the automatic vision system had a success rate of 98.4% in the cutting curves location, 4.2 s time consumption per half-sheep, and approximately 1.3 mm location error. The positioning accuracy and speed of the vision system can meet the requirements of the sheep cutting production line. The vision system shows that there is potential to automate even the most challenging processing operations currently carried out manually by human operators.
Highlights
Lamb carcass segmentation is the basis of carcass deep processing products
After the sheep body is manually fixed on the cutting table, the vision system is started from the man-machine interface software, the system will automatically turn on the Kinect camera to take RGBD images and automatically call the image processing algorithm to calculate the motion control parameters required by the control circuit. e end cutting saw starts to cut the sheep body according to the motion parameters until the task is completed
(1) Semantic segmentation: color images of half-sheep body were used to make a training set for full convolutional neural network (FCN) and Deeplab v3+ models. e trained model was adopted to automatically recognize ribs and spine
Summary
Lamb carcass segmentation is the basis of carcass deep processing products. Good carcass grading standards and assessment technology can establish a good communication platform between consumers and manufacturers and help improve carcass quality and enhance market competitiveness [1]. Can the fast and effective lamb carcass segmentation technology improve the quality of lamb and affect its taste, and it can help to combine the processing procedure with intelligent equipment to improve production efficiency and bring more significant economic benefits [3]. A computer vision algorithm was developed to process images from a Kinect v2 and locate the grasping point in 3D for harvesting operation. Cong et al used binocular cameras to take images of pig carcasses, acquired depth information of the images, determined the movement trajectory by identifying the center line and path points, and guided the robot to complete the pig belly cutting operation [16].
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