Computer Vision News 18 2.4 Possible Challenges Here are possible challenges that you may encounter while implementing this lesson: ➢ Object Detection Relies on Color: Changes in lighting can affect color perception, which may require adjusting the defined color ranges. You can run your code in the debug mode and put a breakpoint right after getting the clusters, and in the variables, see the clusters and their color property. ➢ Visibility of AprilTag and Object: Both must be clearly visible to the camera for accurate detection and positioning. ➢ Handling IK Failures: The script anticipates potential failures in the numerical solution of the IK, indicating either unreachable poses or failure in detecting clusters. 2.5 More on Image Processing: Deep Learning So far, we have handled the Apriltag processing by using conventional approaches. These conventional approaches rely on some handcrafted features by experts. However, as will become apparent in your trials, handcrafted features often produce errors when the environment slightly changes. Slight changes in light intensity might affect the color thresholding, leading to the identification of the wrong cubes. The camera will not be able to perfectly capture the clusters if you’re sitting in a dark room. Deep neural networks can automatically learn hierarchical representations and features from raw data. This allows them to adapt to the inherent complexity and variability in images, potentially uncovering intricate patterns that may be challenging for manual feature engineering. This is why modern literature relies heavily on deep learning techniques. The detection problem is basically treated as an optimization problem, where engineers feed huge amounts of examples Lessons in Robotics
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