Computer vision is not exclusively about deep learning, though deep learning has become a dominant approach for many tasks. While neural networks like CNNs (Convolutional Neural Networks) and transformers have revolutionized areas such as image classification and object detection, traditional computer vision techniques remain relevant. These include methods like edge detection (e.g., Canny edge detector), feature matching (e.g., SIFT or ORB), and image segmentation algorithms (e.g., GrabCut). These techniques are still widely used in scenarios where interpretability, speed, or limited computational resources matter. For example, OpenCV, a popular library, provides many non-deep-learning tools that developers use for real-time applications like augmented reality or robotics navigation.
Deep learning excels in tasks requiring high accuracy with complex data, such as recognizing objects in cluttered scenes or generating image captions. Models like ResNet, YOLO, or Vision Transformers (ViTs) are standard for these problems because they automatically learn hierarchical features from data, reducing the need for manual feature engineering. However, they require large labeled datasets and significant computational power for training. In contrast, traditional methods are simpler to deploy and often perform well in constrained environments. For instance, Haar cascades for face detection are still used in embedded systems where running a deep learning model might be impractical due to hardware limitations.
The choice between deep learning and traditional methods depends on the problem. Hybrid approaches are also common. For example, a pipeline might use traditional image processing to preprocess data (e.g., cropping or thresholding) before applying a neural network. Similarly, SLAM (Simultaneous Localization and Mapping) in robotics combines geometric algorithms with deep learning for depth estimation. While deep learning has expanded the capabilities of computer vision, it’s one tool among many. Developers should evaluate trade-offs like data availability, latency, and hardware constraints when deciding which approach to use.