A neural network in computer vision processes images by learning hierarchical patterns through layers of mathematical operations. The most common architecture is a Convolutional Neural Network (CNN), which uses specialized layers to detect visual features. The input layer takes raw pixel values, and subsequent convolutional layers apply filters (small matrices) to extract edges, textures, or shapes. For example, a filter might highlight vertical edges in the first layer, while deeper layers combine these edges to detect complex structures like eyes or wheels. Pooling layers reduce spatial dimensions, making the network invariant to small shifts in the input. Activation functions like ReLU introduce non-linearity, allowing the model to learn complex relationships. By stacking these layers, the network builds a feature hierarchy, transforming pixels into meaningful representations for tasks like classification.
Training a CNN involves adjusting its parameters to minimize prediction errors. During forward propagation, an image passes through the network, generating a prediction (e.g., labeling a cat). The loss function (e.g., cross-entropy) quantifies the difference between the prediction and the true label. Backpropagation computes gradients of the loss with respect to each parameter, and optimization algorithms like stochastic gradient descent (SGD) update the weights to reduce errors. For example, if the network misclassifies a dog as a cat, gradients indicate how to adjust filter weights to correct this. Techniques like data augmentation (rotating, flipping images) and dropout (randomly disabling neurons) prevent overfitting. Pretrained models like ResNet or VGG16, trained on large datasets like ImageNet, are often fine-tuned for specific tasks, saving computation time.
In practice, CNNs power applications like image classification, object detection, and segmentation. For classification, the final layer uses softmax to output probabilities for each class (e.g., 90% “cat”). Object detection models like YOLO or Faster R-CNN add bounding box regression layers to locate multiple objects in an image. Semantic segmentation networks like U-Net use encoder-decoder architectures to assign class labels to each pixel, useful in medical imaging for identifying tumor boundaries. These models are deployed in real-world systems: self-driving cars use CNNs to detect pedestrians, while facial recognition systems map features for identification. By combining architectural innovations (e.g., skip connections in ResNet) with large-scale training, CNNs achieve high accuracy while remaining computationally efficient for deployment on devices or servers.