The storage container of the drug is related to the length of time the drug can be stored and whether it is contaminated. Usually, there will be character information such as the production batch number on the container. Drug production requires the use of machine vision drug bottle inspection machines. This visual inspection system is usually used at the end of the drug assembly line. Typical inspection items usually include capsules, injections, blisters, packaging labels, etc. The following is a case of detecting surface stains and bottle abnormalities in white liquid medicine bottles.

Liquid medicine bottle defect detection and character optical recognition of bottle body-industrial vision integrator | defect detection | machine vision inspection

Inspection requirements

1. During the bottle inspection process, the bottle should be checked for stains, bottle abnormalities, etc. After the inspection is completed, OK and NG signals should be output and removed to the position of the defective product collection port.

2. Quickly obtain the bottle image, obtain bottle-specific certificate information through image recognition, analysis or calculation, determine whether it meets the requirements, and output the control signal. The main modules of the system are trigger positioning module, image processing module and control module. According to user requirements, when the sample moves to the detection position, the camera is triggered, and the visual system outputs the detection signal to complete the detection function.

Drug appearance defect detection items

1. Bottle defect detection

2. Detection of visible foreign matter and sealing defects in liquid medicine in the bottle

3. Detection of misprinted labels, presence of labels, missing inkjet codes and misalignment of labels

Detection functions:

1. Online detection 200ms/time.

2. Automatic detection of soft plastic plug surface defects.

3. Detection accuracy: 0.5 square millimeter defect.

4. Automatically synchronize the inspected product with the camera to obtain the image.

5. Automatically complete the sample defect detection and saving function.

6. Different specifications of product types can be learned and detected as needed.

7. Automatic storage of product images and historical queries can be performed.