Object Detection

Introduction

Object detection is a fundamental computer vision task that goes beyond simple image classification. While classification tells us what is in an image, object detection tells us both what objects are present and where they are located. This is achieved by predicting bounding boxes around objects of interest along with their class labels and confidence scores.

Object detection is crucial for many real-world applications including autonomous vehicles (detecting pedestrians, cars, and traffic signs), surveillance systems, medical imaging, and augmented reality.

Core Concepts

What is Object Detection?

Object detection combines two tasks:

1. Classification: Determining what objects are present in the image
2. Localization: Determining where those objects are located (bounding boxes)

A bounding box is defined by four coordinates:

• x, y: Top-left corner position
• width, height: Dimensions of the box

Each detection also includes:

• Confidence score: How certain the model is about the detection (0-1)
• Class label: What type of object was detected

Sliding Window Approach

One of the simplest object detection methods is the sliding window approach:

1. Define a window: Choose a fixed-size window (e.g., 8×8 pixels)
2. Slide across the image: Move the window across the image with a certain stride
3. Classify each window: For each position, classify whether it contains an object
4. Collect detections: Windows with high confidence scores become candidate detections

Advantages:

• Simple and intuitive
• Works well for objects of similar sizes
• Easy to implement and understand

Limitations:

• Computationally expensive (many windows to evaluate)
• Fixed window size may not fit all objects
• Generates many overlapping detections

Intersection over Union (IoU)

IoU is a key metric for evaluating object detection:

IoU = Area of Intersection / Area of Union

IoU measures how much two bounding boxes overlap:

• IoU = 1.0: Perfect overlap (identical boxes)
• IoU = 0.5: Moderate overlap (commonly used threshold)
• IoU = 0.0: No overlap

IoU is used for:

• Matching predictions to ground truth boxes
• Evaluating detection quality
• Non-Maximum Suppression

Non-Maximum Suppression (NMS)

Sliding window approaches often produce multiple overlapping detections for the same object. Non-Maximum Suppression removes these duplicates:

1. Sort detections by confidence score (highest first)
2. Select the highest confidence detection
3. Remove all detections with IoU > threshold with the selected detection
4. Repeat until no detections remain

NMS ensures each object is detected only once, keeping the most confident detection.

Algorithm Walkthrough

Training Phase

1. Prepare training data:
   • Images with ground truth bounding boxes
   • Each box labeled with object class
2. Generate training windows:
   • Slide window across each image
   • Label windows as positive (contains object) or negative (background)
   • Use IoU with ground truth to determine labels
3. Train classifier:
   • Learn to distinguish object windows from background
   • Optimize using binary cross-entropy loss
   • Update weights via gradient descent
4. Validation:
   • Test on validation images
   • Compute precision, recall, and F1 score
   • Adjust hyperparameters as needed

Detection Phase

1. Slide window across the test image
2. Extract features from each window position
3. Classify each window (object vs. background)
4. Filter by confidence threshold
5. Apply NMS to remove duplicate detections
6. Return final bounding boxes with confidence scores

Interactive Demo

Use the controls above to experiment with object detection:

• Window Size: Larger windows detect bigger objects but are less precise
• Stride: Smaller strides are more thorough but slower
• Confidence Threshold: Higher values reduce false positives but may miss objects
• IoU Threshold: Controls how aggressively NMS removes overlapping boxes

Watch how the algorithm:

• Slides the detection window across the image
• Predicts confidence scores for each position
• Applies NMS to produce final detections
• Compares predictions to ground truth boxes

Evaluation Metrics

Precision

Precision = True Positives / (True Positives + False Positives)

Measures how many detected objects are actually correct. High precision means few false alarms.

Recall

Recall = True Positives / (True Positives + False Negatives)

Measures how many actual objects were detected. High recall means few missed objects.

F1 Score

F1 = 2 × (Precision × Recall) / (Precision + Recall)

Harmonic mean of precision and recall, providing a single performance metric.

Average Precision (AP)

In practice, object detection is often evaluated using Average Precision, which computes precision at different recall levels and averages them.

Modern Approaches

While this module demonstrates the sliding window approach for educational purposes, modern object detection systems use more sophisticated methods:

Two-Stage Detectors

• R-CNN family (R-CNN, Fast R-CNN, Faster R-CNN)
• First generate region proposals, then classify them
• High accuracy but slower

Single-Stage Detectors

• YOLO (You Only Look Once)
• SSD (Single Shot Detector)
• RetinaNet
• Predict boxes and classes in one pass
• Faster but may sacrifice some accuracy

Key Improvements

• Anchor boxes: Predefined box shapes at different scales
• Feature pyramids: Detect objects at multiple scales
• Attention mechanisms: Focus on relevant image regions
• End-to-end learning: Train the entire system jointly

Use Cases

Autonomous Vehicles

• Detect pedestrians, vehicles, traffic signs, and lane markings
• Real-time processing is critical for safety
• Must handle varying lighting and weather conditions

Surveillance and Security

• Monitor for suspicious activities or unauthorized access
• Track people and objects across camera feeds
• Alert on specific events or behaviors

Medical Imaging

• Detect tumors, lesions, or abnormalities in X-rays, CT scans, MRIs
• Assist radiologists in diagnosis
• Measure and track changes over time

Retail and Inventory

• Count products on shelves
• Detect out-of-stock items
• Monitor customer behavior and traffic patterns

Augmented Reality

• Detect real-world objects to overlay digital content
• Enable interactive experiences
• Track objects for stable AR placement

Best Practices

Data Preparation

• Diverse training data: Include various object sizes, poses, and lighting
• Accurate annotations: Precise bounding boxes are crucial
• Data augmentation: Flip, rotate, and scale images to increase variety
• Balance classes: Ensure all object types are well-represented

Model Configuration

• Window size: Should match typical object sizes in your images
• Stride: Balance between speed and detection density
• Confidence threshold: Tune based on precision/recall trade-off
• IoU threshold: Lower values keep more detections, higher values are more aggressive

Performance Optimization

• Multi-scale detection: Use multiple window sizes for objects of different scales
• Feature extraction: Use powerful features (e.g., from CNNs)
• GPU acceleration: Essential for real-time applications
• Model compression: Reduce model size for deployment on edge devices

Common Pitfalls

• Too many false positives: Increase confidence threshold or improve features
• Missing small objects: Reduce stride or use smaller windows
• Slow inference: Increase stride, use fewer scales, or optimize model
• Poor localization: Improve training data or use regression for box refinement

Further Reading

Foundational Papers

• R-CNN: "Rich feature hierarchies for accurate object detection" (Girshick et al., 2014)
• YOLO: "You Only Look Once: Unified, Real-Time Object Detection" (Redmon et al., 2016)
• SSD: "SSD: Single Shot MultiBox Detector" (Liu et al., 2016)
• Faster R-CNN: "Faster R-CNN: Towards Real-Time Object Detection" (Ren et al., 2015)

Modern Advances

• EfficientDet: Scalable and efficient object detection
• DETR: Detection Transformer (end-to-end with transformers)
• YOLOv8: Latest in the YOLO series with improved accuracy and speed

Tutorials and Resources

• PyTorch Object Detection Tutorial
• TensorFlow Object Detection API
• Papers with Code - Object Detection benchmarks
• COCO Dataset (Common Objects in Context)

Related Topics

• Instance Segmentation (pixel-level object detection)
• Semantic Segmentation (classify every pixel)
• 3D Object Detection (detect objects in 3D space)
• Video Object Detection (temporal consistency)

Summary

Object detection is a powerful computer vision technique that enables machines to not only recognize objects but also locate them precisely in images. While the sliding window approach demonstrated here provides an intuitive introduction, modern deep learning methods have dramatically improved both accuracy and speed.

Key takeaways:

• Object detection combines classification and localization
• Sliding windows provide a simple but effective approach
• IoU measures bounding box overlap quality
• Non-Maximum Suppression removes duplicate detections
• Precision and recall trade-offs are important to understand
• Modern methods use deep learning for end-to-end detection

Understanding these fundamentals prepares you to work with state-of-the-art object detection systems and apply them to real-world problems.