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Software automates image-recognition in assembly lines

Posted: 10 Sep 2014 ?? ?Print Version ?Bookmark and Share

Keywords:image recognition? assembly? Fujitsu?

Fujitsu Laboratories Ltd. developed a technology for automatically generating image-recognition programs that accurately detect the positions of components in assembly processes.

Fujitsu's technology detects positions by controlling the order in which the various image-processing functions that make up a program are combined, and using machine learning based on the similarity of shapes. Samples of the object to be detected are presented as teaching materials, making it possible to automatically generate an image-recognition program in roughly eight hours, or one-tenth the time previously required.

Modern production lines use automated assembly equipment and automated inspection equipment that rely on cameras. Processing the images from these cameras has in the past required custom software developed by experts. Any changes to the manufacturing setup, such as a machine's operating parameters, could involve more than a week's time spent revising the program, during which time the production line would sit idle.

Image recognition

Figure 1: The conventional process of developing and revising an image-recognition program used with a camera in automated assembly equipment. Source: Fujitsu

Automatically generated recognition programs already use genetic algorithms, a type of machine learning. With genetic algorithms, two programs, which will become the parents, are randomly selected from multiple image-recognition programs. The parents are then merged together to create a number of child image-recognition programs.

Each of these child programs then undergoes an evaluation that uses previously prepared training images and a set of target pictures, which show the features that are intended to be extracted from the pictures (the correct-answer set). The training pictures are fed into the child programs, and the output pictures are scored based on how well they match up with the target pictures. The child programs are then culled based on their scores, with the highest-scoring ones becoming the parents to the next generation. Those that pass become the next parent generation, and the process is repeated until a child with a passing score appears through an evolutionary process.

The method is well-suited to image manipulations, such as emphasizing a target region of the image, but the time required for the machine-learning process is highly dependent on the number of members in the parent generation, and therefore can take a long time. Furthermore, this method cannot be used to automatically generate programs that will accurately detect the position of a component within an image.

Image recognition

Figure 2: How image-recognition programs are created automatically using genetic algorithms. Source: Fujitsu

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