For small pieces bin picking project, is deep learning necessary. Is the deep learning robust in production?
There are a few factors to consider when deciding if deep learning is necessary or best suited for a small parts bin picking application:
- Deep learning can be very effective for bin picking, as it can learn to recognize and locate objects in cluttered environments. However, deep learning models require large amounts of training data which may be difficult to obtain for niche or small-batch industrial applications.
- Simpler machine vision approaches like template matching or feature-based methods may be sufficient for constrained environments with consistent lighting and a limited number of parts. These classical methods require less or none training data.
- Deep learning approaches tend to be more robust to changes in lighting, viewpoint, and background clutter. This can be important for real-world variability in production environments. But deep models may be more prone to unpredictable failures if the test data differs too much from the training data.
- Deep learning would require more upfront effort in data collection, but could pay off in the long run with greater flexibility.
- For deployment in production, extensive testing is required for any approach to ensure the system is reliable in the operating conditions. Monitoring and re-training may be required over time as conditions evolve.