In a notable advancement for machine vision technology, researchers at the University of Cordoba have successfully developed a neural network-based model that enables the detection and decoding of fiducial markers under low-light conditions, thereby overcoming a significant limitation in the field. Fiducial markers, which resemble high-contrast black and white square codes, are crucial guides for robots and machines to detect and determine the location of objects, with applications in logistics, robotics, and automation. By leveraging neural networks, the model, known as DeepArUco++, can accurately locate and decode these markers even in challenging lighting situations, paving the way for more efficient and reliable machine vision applications. This innovative solution, which involves a three-step process of marker detection, corner refinement, and marker decoding, has been trained on a synthetic dataset that simulates real-world lighting conditions and has been tested with actual data, demonstrating its effectiveness in overcoming the limitations of traditional machine vision techniques. With the release of the code and open access to the training data, this breakthrough has the potential to be widely adopted and applied in various industries, enabling machines to navigate and interact with their environment more effectively, even in low-light conditions.
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