Object Detection Revolution

Object detection identifies and locates multiple objects in images/video. Modern models achieve 90%+ mAP (mean Average Precision) at 30+ FPS, enabling real-time applications in autonomous vehicles, security, retail, and manufacturing.

Key Algorithms

1. YOLO (You Only Look Once)

• YOLOv8: Latest version, state-of-the-art
• Speed: 30-100 FPS on GPU (real-time)
• Accuracy: 70-90% mAP depending on task
• Best for: Real-time applications (video, robotics)
• Easy to use: Ultralytics library

2. Faster R-CNN

• Two-stage detector (proposal + classification)
• Accuracy: 80-95% mAP (higher than YOLO)
• Speed: 5-10 FPS (slower than YOLO)
• Best for: High-accuracy needs, not real-time

3. EfficientDet

• Balanced speed and accuracy
• Scalable architecture (D0-D7)
• Good efficiency for edge deployment

4. RetinaNet

• Single-stage with focal loss
• Handles class imbalance well
• Good for small objects

Applications

Autonomous Vehicles

• Pedestrian, vehicle, traffic sign detection
• Real-time processing (30+ FPS required)
• Safety-critical application
• Multi-object tracking

Security & Surveillance

• Intrusion detection
• Person re-identification
• Weapon detection
• Crowd monitoring
• 24/7 automated monitoring

Retail & Inventory

• Shelf monitoring and out-of-stock detection
• Customer behavior analytics
• Checkout-free stores (Amazon Go style)
• Product counting and tracking

Manufacturing Quality Control

• Defect detection on production lines
• Part identification and sorting
• Assembly verification
• 90-99% defect detection rate
• Real-time inspection (no slowdown)

Implementation Guide

Step 1: Data Collection

• 1K-10K labeled images per class
• Diverse conditions (lighting, angles, backgrounds)
• Annotation tools: LabelImg, CVAT, Roboflow

Step 2: Model Selection

• Real-time: YOLOv8
• High accuracy: Faster R-CNN
• Balance: EfficientDet
• Edge devices: MobileNet-SSD, YOLO-Nano

Step 3: Training

• Transfer learning from pre-trained models (COCO dataset)
• Fine-tune on your data (50-200 epochs)
• Data augmentation (rotation, flip, color jitter)
• Training time: 1-10 hours on GPU

Step 4: Evaluation

• mAP (mean Average Precision): Primary metric
• IoU (Intersection over Union): Bounding box accuracy
• FPS: Inference speed
• Test on diverse test set

Step 5: Deployment

• Cloud: AWS, Azure, GCP inference
• Edge: NVIDIA Jetson, Raspberry Pi, TPU
• Optimization: TensorRT, ONNX for speed

Best Practices

• Quality Data: Accurate annotations critical
• Class Balance: Equal samples per class
• Multi-scale Training: Detect objects at various sizes
• Test Time Augmentation: Boost accuracy by 2-5%
• Post-processing: NMS (Non-Max Suppression) to remove duplicates

Case Study: Manufacturing Defect Detection

• Challenge: Manual inspection, 8% defect escape rate
• Solution: YOLOv8 for real-time defect detection
• Data: 5K images, 8 defect types
• Results:
  • Defect detection: 96% accuracy
  • False positives: 2% (vs 15% initial)
  • Inspection speed: 100% (real-time, no slowdown)
  • Defect escape: 8% → 0.4% (-95%)
  • ROI: ₹2.8Cr/year (reduced defects + labor)

Pricing

• Basic System: ₹12-25L (single camera, YOLOv8)
• Multi-camera: ₹30-60L (5-10 cameras)
• Enterprise: ₹80L-3Cr (100+ cameras, custom models)