How Object Detection Works
Object detection combines image classification with localization. Unlike simple classifiers that label an entire image, object detectors identify multiple objects and their positions. Modern detectors like YOLO use convolutional neural networks (CNNs) to extract features from the image and predict bounding box coordinates, class labels, and confidence scores in a single forward pass.
Understanding YOLO Architecture
YOLO divides the input image into an S x S grid. Each grid cell predicts a fixed number of bounding boxes with confidence scores and class probabilities. Non-maximum suppression (NMS) removes duplicate detections. This single-shot approach makes YOLO significantly faster than two-stage detectors like R-CNN while maintaining competitive accuracy.
Confidence Scores and Thresholds
Each detection comes with a confidence score between 0 and 1 representing the model's certainty. The intersection over union (IoU) metric measures how well a predicted box overlaps with the actual object. By adjusting the confidence threshold, you trade off between precision (fewer false positives) and recall (fewer missed objects).
Applications of Object Detection
Object detection powers autonomous vehicles (pedestrian and vehicle recognition), security surveillance (intrusion detection), retail analytics (shelf monitoring and customer counting), medical imaging (tumor localization), industrial quality control (defect detection), and augmented reality (scene understanding and object interaction).
