Object Detection
Learn how object detection works — from R-CNN to YOLO — and build models that can locate and classify multiple objects in images simultaneously. This is a foundational concept in artificial intelligence and machine learning that professional developers rely on daily. The explanations below are written to be beginner-friendly while covering the depth and nuance that comes from real-world AI/ML experience. Take your time with each section and practice the examples
Object Detection vs Classification
While image classification answers 'What is in this image?', object detection answers 'What objects are in this image, WHERE are they, and HOW confident are we?'. Detection outputs bounding boxes (x, y, width, height) with class labels and confidence scores for every object found. This is fundamental for autonomous driving (detecting cars, pedestrians, signs), medical imaging (finding tumors), retail (shelf analysis), security (person detection), and robotics (object manipulation). The challenge is speed — methods must process frames in real-time (30+ FPS) for video applications while maintaining accuracy.
Evolution of Object Detection
- R-CNN (2014): Region-based CNN — generates ~2000 region proposals, runs CNN on each. Accurate but slow (47 seconds per image)
- Fast R-CNN (2015): Runs CNN once on entire image, then extracts features for each region. 25x faster than R-CNN
- Faster R-CNN (2016): Introduces Region Proposal Network (RPN) — end-to-end trainable, 5 FPS. The foundation for many modern detectors
- SSD (2016): Single Shot MultiBox Detector — detects at multiple scales in a single forward pass. 59 FPS with good accuracy
- YOLOv1-v8 (2016-2023): You Only Look Once — frames detection as regression. Each version faster and more accurate. YOLOv8 achieves real-time detection on edge devices
- DETR (2020): DEtection TRansformer — applies transformers to detection, eliminating anchor boxes and NMS. Simpler architecture, competitive accuracy
- YOLOv9/v10 (2024): Latest improvements with programmable gradient information and NMS-free training — state-of-the-art speed-accuracy trade-off
Key Detection Metrics
- IoU (Intersection over Union): Measures overlap between predicted and ground truth boxes — IoU > 0.5 is typically 'correct'
- Precision: Of all detections made, how many were correct? High precision = few false positives
- Recall: Of all actual objects, how many were detected? High recall = few missed objects
- mAP (Mean Average Precision): The gold standard metric — average precision across all classes at different IoU thresholds
- FPS (Frames Per Second): Speed of inference — real-time applications need 30+ FPS, video surveillance needs 15+ FPS
- mAP@0.5: Average precision when IoU threshold is 0.5 (lenient). mAP@0.5:0.95: Average across IoU 0.5 to 0.95 (strict)