Development of artificial intelligence system for the detection of parts in production

The pieces arrived at a specific point on the production line by sliding from ramps, so that they were in random positions and orientations, causing problems for the robot. For this reason, an artificial vision system was installed to detect the lids, allowing the robot to operate correctly.

Therefore, from the Product Development area, we developed a system that uses deep learning applied to computer vision for object detection. Through the design of neural networks, we were able to successfully detect the position and orientation of all the lids on the tray with a hit rate of over 99%, meeting the objectives set by the client and successfully overcoming the considerable challenges posed by this case.

This project required complex development, which was carried out on several levels. Our team programmed the camera management software, the deep learning software using neural networks and the system’s user interface software. All of these used the Python programming language, the most widely used language for artificial intelligence and computer vision, and various camera management and neural network training libraries, such as TensorFlow and PyTorch.

The last step was to implement the artificial vision system on the production line, using industrial cameras, a computer with a GPU to run the inference calculations on the captured images and speed up the computation time, and screens to visualise the results. Once the assembly was completed, we proceeded to start up the system, checking its correct operation and correcting any possible incompatibilities with the final hardware.