Introduction
Build a real-time multi-class object detection system using YOLOv8, custom dataset training, and edge deployment. This comprehensive guide covers everything from design through implementation, testing, and deployment.
Computer Vision Object Detection System
Build a real-time multi-class object detection system using YOLOv8, custom dataset training, and edge deployment.
DifficultyAdvanced
Duration4–5 weeks
Components10 items
Steps6 steps
Theory & Background
Collect 500–2000 images per class representing your target objects. Sources: capture with camera, download from Open Images, use Google Images. Annotate using LabelImg: draw bounding boxes and assign class labels. Export in YOLO format: each image has a .txt file with lines
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Components & Requirements
10 components required for this project.
| # | Component | Purpose | Qty |
|---|---|---|---|
| 1 | Python 3.10+ | Main language | x1 |
| 2 | YOLOv8 (Ultralytics) | State-of-the-art object detection model | x1 |
| 3 | OpenCV | Image/video processing pipeline | x1 |
| 4 | Roboflow | Dataset management and annotation | x1 |
| 5 | CUDA GPU (NVIDIA) | Training acceleration | x1 |
| 6 | TensorRT or ONNX | Edge deployment optimization | x1 |
| 7 | Raspberry Pi 4 or Jetson Nano | Edge inference hardware | x1 |
| 8 | USB Camera or IP Camera | Live video feed | x1 |
| 9 | LabelImg | Bounding box annotation tool | x1 |
| 10 | Weights & Biases (wandb) | Training experiment tracking | x1 |
Step-by-Step Implementation
Follow these 6 steps carefully.
1
Custom Dataset Creation and Annotation
Collect 500–2000 images per class representing your target objects. Sources: capture with camera, download from Open Images, use Google Images. Annotate using LabelImg: draw bounding boxes and assign class labels. Export in YOLO format: each image has a .txt file with lines
2
YOLOv8 Training
Create dataset.yaml: specify train/val/test paths and class names. Train command: yolo detect train model=yolov8n.pt data=dataset.yaml epochs=100 imgsz=640 batch=16. YOLOv8n (nano): 3.2M parameters, fastest. YOLOv8s (small): 11.2M, balanced. YOLOv8m (medium): 25.9M, higher accuracy. Training on Colab free GPU: 100 epochs for 500-image dataset takes 1–2 hours for nano model. Monitor mAP50 (mean Average Precision at IoU threshold 0.5) — target > 0.8.
3
Model Evaluation and Analysis
Evaluate on test set: precision, recall, mAP50, mAP50-95. Confusion matrix shows which classes are confused with each other. PR (Precision-Recall) curve shows model performance at different confidence thresholds. False positive analysis: common causes are partial occlusion, unusual viewpoints, or class imbalance. False negative analysis: missed detections often at image edges or small object scales. Use these insights to collect targeted additional training data.
4
Real-Time Inference Pipeline
OpenCV video pipeline: cap = cv2.VideoCapture(0). results = model(frame). For each detection: draw bounding box (cv2.rectangle), label (cv2.putText), confidence score. Implement tracking: SORT or ByteTrack maintains object IDs across frames — preventing ID flickering for moving objects. FPS optimization: process every 2nd frame at full resolution, interpolate bounding boxes for alternate frames — doubles throughput.
5
Edge Deployment with TensorRT
Export trained YOLOv8 to TensorRT: model.export(format=
6
Deployment as REST API
Wrap model in FastAPI endpoint: POST /detect with image upload, returns JSON with detected objects (class, confidence, bounding box coordinates). Add authentication (API key), rate limiting, and image size validation. Docker containerize for consistent deployment. For multiple cameras: implement a producer-consumer architecture with Redis queue — cameras push frames, GPU workers process and store results, clients poll for latest detections.
Code & Implementation
Core code for detection_api.py:
detection_api.py
Python
Testing & Troubleshooting
Test Computer Vision Object Detection System by verifying each subsystem individually before full integration.
Troubleshooting Tips
Verify power voltages, check ground connections, use serial monitor for debug.
Real-World Applications
Industrial quality control and defect detection
Retail shelf analytics and inventory
Traffic monitoring and vehicle counting
Security surveillance anomaly detection
Medical image analysis (pathology)
Agricultural crop disease detection
Sports performance analysis
Autonomous vehicle perception
Extensions & Next Steps
- Implement instance segmentation (YOLOv8-seg) for pixel-precise masks
- Add multi-camera synchronization for 3D object localization
- Build an active learning pipeline for efficient data collection
- Implement few-shot learning for new classes with minimal data
- Add adversarial robustness testing for deployment security
Interactive Playground
Coming SoonAn interactive simulator will be available here — simulate circuits and run code in-browser without hardware.
Frequently Asked Questions
What is the difference between YOLO, SSD, and R-CNN object detection architectures?
R-CNN family (R-CNN, Fast R-CNN, Faster R-CNN): two-stage detectors — first propose regions likely containing objects, then classify each region. High accuracy but slow (7 fps for Faster R-CNN). SSD (Single Shot Detector): one-stage, predicts objects at multiple scales simultaneously — faster than Faster R-CNN but less accurate for small objects. YOLO (You Only Look Once): one-stage, divides image into grid, each cell predicts objects — fastest of all, YOLOv8 achieves 300+ fps on GPU while maintaining near Faster R-CNN accuracy.
How much training data do I need for a custom object detector?
As a general guideline: 100 images per class for simple, distinct objects (coins, playing cards). 500–1000 per class for complex objects (vehicles, animals in varied poses). 2000+ per class for challenging detection (small objects, heavy occlusion, high inter-class similarity). With transfer learning from COCO pre-trained YOLOv8, even 50–100 high-quality images per class can produce useful detectors for simple cases. Data quality (correct annotations, diversity) matters more than quantity.
What is mAP and how should I interpret it?
mAP (mean Average Precision) is the standard metric for object detection. AP per class: area under the Precision-Recall curve for that class. mAP: average across all classes. mAP50: AP at IoU threshold 0.5 (bounding box overlap ≥ 50% to count as correct). mAP50-95: averaged at IoU thresholds 0.5, 0.55, 0.6...0.95 — more rigorous, penalizes imprecise localization. mAP50 > 0.8 is generally good for industrial use. mAP50-95 > 0.5 is state-of-the-art level performance.
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