Research on Automatic Inspection of PP-bag Mushroom Packaging

• Main Authors: LI, TSENG-WEI, 李增偉
• Other Authors: JOU, RONG-YUAN
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/rc8537

碩士 === 國立虎尾科技大學 === 機械設計工程系碩士班 === 106 === At present, The mushroom industry is still one of Taiwan's important industries. Although Taiwan's agricultural cultivation technology is skillful, the degree of automation of agriculture is relatively low compared with other advanced countries.The main purpose of the research is to use the image recognition method of Deep Learning algorithm to solve the removal of products on the automatic production line of mushroom PP-bag. This study uses the robotic arm (Robot) combined with visual module to realize the elimination of defective products on the production line by using the depth learning method, and to implement the mechanical arm multitask system with the Seventh Axis. Because of the complexity of the object range and condition of identification, we use the structure light of the projection led light source to project to the object to be tested, and use the structure light projection method to effectively identify the large range and complex object, combine with the dual-lens CMOS industrial camera to retrieve the image, by software computing to create a 3D point cloud image of the object, we can get detailed information about the object and build a image database of mushroom PP-bag. The deep learning training is performed by the Labeled training dataset to achieve image recognition. The system uses the GPU and uses the CUDA architecture to train the deep learning model, saving training time and improving object recognition rate. Using adaptive robot gripper combined with fixture to indeed capture the mushroom PP-bag. By changing the clamping force and the characteristics of the opening and closing, adjust the best clamping posture, not destroy the space package structure and internal holes so that it can not be reused.The experimental results show that the depth learning algorithm has an average recognition confidence of up to 0.989 in the number of images and the stable ambient light source. Adaptive robot gripper and fixtures is able to accurately capture PP bags even though repeated gripping，The aperture in the same mushroom PP-bag can also be maintained in the standard range of 25mm or more without excessive deformation.