Object detection in computer vision is a technique that identifies and locates specific objects within images or videos. Unlike image classification, which assigns a single label to an entire image, object detection pinpoints multiple objects by drawing bounding boxes around them and labeling each. This process enables systems to understand both what objects are present and where they are located. For example, in a street scene, object detection can identify cars, pedestrians, and traffic lights simultaneously, providing spatial context critical for applications like autonomous driving.
Object detection typically involves two main steps: feature extraction and localization/classification. Early methods, like Haar cascades or Histogram of Oriented Gradients (HOG), relied on handcrafted features to detect objects based on edges or textures. Modern approaches use deep learning models, such as Convolutional Neural Networks (CNNs), which automatically learn hierarchical features from data. Models like Faster R-CNN, YOLO (You Only Look Once), and SSD (Single Shot MultiBox Detector) combine region proposal networks or grid-based systems to predict bounding boxes and class probabilities in one or two stages. For instance, YOLO divides an image into a grid and predicts bounding boxes directly, trading some accuracy for real-time speed, while Faster R-CNN uses region proposals for higher precision at a computational cost.
Practical applications of object detection span industries. In retail, it can track inventory by identifying products on shelves. In healthcare, it assists in analyzing medical images to locate anomalies like tumors. Autonomous vehicles rely on it to detect obstacles, lane markings, and traffic signs. However, challenges remain, such as handling occluded objects, varying lighting conditions, or balancing speed and accuracy. Developers often fine-tune pre-trained models (via transfer learning) on domain-specific datasets to address these issues. Tools like TensorFlow’s Object Detection API or PyTorch’s Detectron2 provide frameworks to implement these models efficiently, emphasizing modular architectures and optimization for deployment on edge devices.