Abstract: The present invention provides an automated optical inspection and classification apparatus based on a deep learning system, comprising a camera and a processor. The processor executes a deep learning system after loading data from a storage unit and the processor, and comprises an input layer, a neural network layer group, and a fully connected-layer group. The neural network layer group is for extracting to an input image and thereby obtaining a plurality of image features. The fully connected-layer group is for performing weight-based classification and outputting an inspection result.

Abstract: A labeling system for defect classification having a storage unit and a processing unit is provided. The storage unit stores a defect classification module, a defect labeling module, and a catalog generation module. The processing unit performs the modules aforementioned. The defect classification module is configured to provide a defect type information. The defect labeling module marks a defect type label to at least one test object image according to an image feature and the defect type information on the at least one test object image. The catalog generation module receives the labeled test object images and moves test object images having the same defect type label to a corresponding file catalog.

Abstract: A training method of an iterative deep learning system, comprising the steps of: providing at least one unlabeled image of an object; labeling the unlabeled image of the object to produce a labeled image of the object; storing the labeled image of the object into an image database if the deep learning system is being trained with a labeled version of the unlabeled image of the object for a first time; identifying the labeled image of the object in the image database and outputting an identification result through the deep learning system; and training the deep learning system according to an error between output and expected output of the identification result; and providing another unlabeled image of the object to the trained deep learning system to identify said another unlabeled image of the object and to produce an inspection result; wherein determining whether the inspection result has satisfied a qualification condition and terminating training if the inspection result has satisfied the qualification co

Abstract: The present invention provides an automated optical inspection and classification apparatus based on a deep learning system, comprising a camera and a processor. The processor executes a deep learning system after loading data from a storage unit and the processor, and comprises an input layer, a neural network layer group, and a fully connected-layer group. The neural network layer group is for extracting to an input image and thereby obtaining a plurality of image features. The fully connected-layer group is for performing weight-based classification and outputting an inspection result.

Abstract: A safety syringe assembly includes a syringe and a concealing apparatus. The syringe includes a cylinder, two wings formed on two opposite sides of the cylinder, a needle connected to a front end of the cylinder, and a tube connected to a rear end of the cylinder. The concealing apparatus includes a guiding element and two covers. The guiding element is located around the tube. The two covers are engaged with each other and movable along the guiding element between an original position for exposing the guiding element and a concealing position for concealing the needle.