LEARNING
It comprehends and automatically assimilates the distinctive visual features of objects.
This algorithm relies on machine learning and convolutional neural networks (CNN) for extracting relevant features and training search models.
TRAINING DATA
During this phase, the algorithm is fed with a large set of training data containing labeled images indicating the presence or absence of the objects of interest.
CNN
Convolutional neural networks automatically learn the distinct visual features of objects, such as shapes, textures, or contours, enabling the algorithm to identify them accurately.
Applications
Once trained, the object detection algorithm can be applied to identify objects of interest in new images or videos. The algorithm analyzes the image or video frame by frame, searching for relevant features learned during training. When objects of interest are detected, the algorithm provides information about their position and size, allowing for further processing or user interaction.
PRODUCTION LINES
It verifies the presence or absence of specific components or parts on moving products, such as identifying caps on bottles or labels on packaged goods.
RECOGNITION OF SPECIFIC COMPONENTS
It identifies specific components among a variety of similar parts, for example, recognizing chips or specific devices on a motherboard or silicon wafer.
ASSEMBLY VERIFICATION
It verifies that components have been correctly assembled, such as identifying the correct arrangement of screws, connectors, or cables within a product.