The claim that "a tiny NN can learn edges better" is misleading. Classical algorithms like Canny or Sobel are specifically designed for edge detection, making them faster, more reliable, and easier to use in controlled environments. Neural networks need more data, training, and computational power, often just to achieve similar results. For simple edge detection tasks, classical methods are typically more practical and efficient.
For clean, classical edges in standard images, classical algorithms(Canny, Sobel) are hard to beat in terms of accuracy, efficiency, and clarity.
For domain-specific edges (e.g., medical images, low-light, or noisy industrial setups), a small, well-trained neural network may perform better by learning more complex patterns.
In summary, "Garbage In, Garbage Out" applies to both classical algorithms and neural networks. Good camera setup, lighting, and optics solve 90% of machine vision or computer vision problems before the software—whether classical or neural network-based—comes into play.
