The visual labeling machine can recognize transparent labels or reflective materials, but it requires specific technical means and equipment configuration to achieve. The following is a specific analysis:

1、 Identification technology of transparent labels

Capacitive sensor

Some visual labeling machines are equipped with capacitive label sensors, which recognize transparent labels by detecting changes in the capacitance of the labels.

For example, capacitive sensors can stably detect transparent film labels, even if the label surface has no reflective properties.

Transparent background sticker assistance

Attach a transparent background sticker on the back of the transparent label to increase the reflective area and enable the photoelectric sensor to recognize the label information.

This method indirectly achieves the recognition of transparent labels by changing the optical properties of the labels.

High precision visual algorithm

Using deep learning algorithms, the model is trained to recognize the edges or feature points of transparent labels.

For example, some visual labeling machines can recognize small texture differences in transparent labels through visual algorithms.

2、 Identification technology of reflective materials

Polarized light technology

Use polarized light sources and polarized filters to eliminate specular reflection of reflective materials and preserve diffuse reflection information of labels.

This method can effectively reduce reflective interference and improve label recognition rate.

MultiSpectral Imaging

Using a multispectral camera to image different wavelengths of light, distinguish the spectral characteristics of reflective materials and labels.

For example, some visual labeling machines can use near-infrared spectroscopy to identify labels on reflective surfaces.

Structured light projection

Project structured light patterns onto the surface of reflective materials, and determine the label position through the recognition of deformed patterns.

This method is suitable for label recognition on curved or irregular reflective surfaces.

3、 Equipment configuration and optimization

high resolution camera

Use high-resolution industrial cameras (such as those with over 12 million pixels) to enhance the ability to capture image details.

For example, high-resolution cameras can clearly recognize tiny text or patterns on transparent labels.

Intelligent Light Source System

Equipped with adjustable angle and brightness light sources, the lighting conditions are automatically adjusted according to the label material.

For example, for reflective materials, the brightness of the light source can be reduced and the angle adjusted to minimize reflective interference.

adaptive algorithm

Adopting an adaptive threshold segmentation algorithm, the recognition parameters are automatically adjusted based on the label background.

For example, in transparent label recognition, the algorithm can dynamically adjust the contrast threshold to improve recognition stability.

4、 Practical application cases

3C electronics industry: Some visual labeling machines can stably recognize transparent labels on mobile phone screen protectors, with a labeling accuracy of ± 0.1mm.

Pharmaceutical industry: When attaching drug information labels on reflective glass bottles, a 99.9% recognition rate is achieved through polarized light technology.

Logistics industry: For express packages wrapped in transparent tape, capacitive sensors are used to achieve efficient sorting and label recognition.

5、 Technical limitations and solutions

The influence of label thickness: Ultra thin transparent labels (<0.05mm) may affect the detection accuracy of capacitive sensors, which can be solved by increasing the thickness of the label backing adhesive.

Environmental light interference: In strong light environments, the recognition rate of reflective materials may decrease, and it is necessary to use light shields or visual algorithms that resist environmental light interference.