Developing visual recognition technology ranges from straightforward to challenging, based on the complexity of the application. Simple tasks like barcode scanning can be implemented using off-the-shelf tools like OpenCV.
Advanced applications, such as facial recognition or object detection in dynamic environments, require deep learning expertise, access to annotated datasets, and computational resources for training. Ensuring robustness and accuracy across diverse scenarios, such as lighting variations or occlusions, adds to the difficulty.
Frameworks like TensorFlow and PyTorch, along with pre-trained models, simplify the process, but fine-tuning for specific use cases still demands considerable skill and effort.