Hi, deep learning enhances machine vision in object recognition by improving accuracy, automating feature extraction, effectively handling large datasets, enabling real-time processing, and offering scalability and adaptability.

In my view, Deep learning models can benefit from transfer learning, where pre-trained models on large datasets are fine-tuned for specific object recognition tasks. This is particularly useful when dealing with limited labeled data for a specific application. Transfer learning allows the model to leverage knowledge gained from general datasets, improving performance on specific tasks.Also, deep learning models can be trained on augmented datasets, which include variations of the original data (e.g., different orientations, lighting conditions, and backgrounds). This helps the model generalize better to real-world scenarios and improves its robustness in recognizing objects under various conditions.

Dear S M Mohiuddin Khan Shiam ,

Identification. Image classification using deep learning categorizes images or image regions to distinguish between similarly looking objects including those with subtle imperfections. Image classification can, for example, determine if the lips of glass bottles are safe or not.

https://imaging.matrox.com/en/technology/deep-learning#:~:text=Real%2Dworld%20examples-,Identification,bottles%20are%20safe%20or%20not.

Regards,

Shafagat