In actual production, LCD screens are cut into different shapes according to design requirements, as well as during cleaning, transportation, and other processes, which can easily cause defects such as cracks, shells, broken edges, and corners at the edges. These defects are generally very small, and the cutting edges are easily affected by uneven lighting, grayscale gradients, and cutting shapes, making edge defect detection difficult. Defects may cause LCD screen breakage and electrode damage during subsequent production and use, which will become quality hazards. It is necessary to promptly remove defective products through testing equipment to avoid them flowing into downstream processes and wasting material and labor hours.
The LCD screen edge inspection machine developed and produced by Fuxin Optoelectronics based on machine vision and AI artificial intelligence technology integrates light, machine, electricity, software, and algorithms. It is mainly used for automatic detection of edge defects in the LCD screen production process, achieving machine replacement.
High precision and high-speed optical imaging system with independent intellectual property rights ensures high-quality edge imaging.
The core algorithm for defect detection with proprietary intellectual property rights and AI deep learning re evaluation algorithm are at the forefront of the industry.
By using deep learning based object detection algorithms, defects can be accurately identified, located, and quantified, improving defect detection rates and yield rates, reducing customer complaints and cost waste.
Innovatively adopting an I-shaped seismic scanning mechanism and a marble seismic platform to achieve stable operation.
The TFT-LCD edge inspection machine is developed and produced by Fuxin Optoelectronics. Based on machine vision and AI artificial intelligence technology, the TFT-LCD edge inspection machine integrates optics, mechanics, electronics, software, and algorithms. It is mainly used for automatic detection of edge defects in the TFT-LCD production process, achieving machine replacement. In practical applications, suspected defect areas are extracted from the edge sample images of TFT-LCDs after image processing, and judgment criteria are established to effectively eliminate interference. The types of defects such as edge extension, shell, edge collapse, and corner collapse are successfully determined, and the criteria are established to avoid repeated judgments and detect multiple defects simultaneously.
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