Automated Aoi Vision System Machine For Products Made From Feathers

Inspection Principle

In the feather classification industry, there is no relatively efficient machine and equipment to classify feather grades. The traditional way is to rely on manual detection and classification, which has low detection efficiency and can not guarantee the accuracy. In order to improve the efficiency of feather sorting, the detection accuracy is improved.

Badminton is a common sports equipment. In the selection of badminton materials, badminton balls made of duck feathers are favored by many schools and families because of their affordable prices. But before the duck feathers are made into badminton, each duck feather must be cleaned, bleached, dried, and measured. Then classify the duck feather level (level 1, 2, 3, 4, 5, 6), radius, fold printing, trimming and other categories, and place feathers of the same size and angle together. Therefore, the treated duck feathers pass through a sorting machine with 26 sensors, and the airflow of the conveyor belt can blow the feathers into the holes, and then classify the duck feathers according to different detection requirements.

Equipment Overviews&Parameters

Traditional image processing algorithms need to design different image processing rules for different scenes. With the increase of scenes, the image processing rules will become more and more complex, and the recognition rate will be lower and lower. The deep learning algorithm used by this machine greatly improves the accuracy of feather classification by training a large number of sample data, which is more efficient than traditional image recognition algorithms. The algorithm has the function of identifying and classifying different types of duck feathers by training different levels of duck feather pictures without adding or modifying the code, and assists in realizing the automation of duck feather detection more efficiently.

In the feather classification industry, there is no relatively efficient machine and equipment to classify feather grades. The traditional way is to rely on manual detection and classification, which has low detection efficiency and can not guarantee the accuracy. In order to improve the efficiency of feather sorting and improve the detection accuracy, Keyi Technology developed the efficient intelligent feather sorting machine. The equipment mainly uses artificial intelligence deep learning to classify feather species, with high detection efficiency and precision, which can effectively replace manual detection and greatly improve the detection efficiency. The equipment adopts deep learning, which greatly improves the accuracy of feather classification by training a large number of sample data, and is more efficient than traditional image recognition algorithms.

Machining Process