目标检测

1. requirements

Functional Requirements

  • 检测对象 Multi-class: cars, pedestrians, animals or weapon

  • Cloud based or device based

Non-functional

  • Target mAP > 0.75 at IOU 0.5

  • Latency < 100ms per frame for real-time applications

  • Scalable to handle multiple concurrent requests

  • High availability (99.9%)

2. ML task & pipeline

Input Stream -> Preprocessor -> Detection Model -> Post-processor -> Output
     ↑                             ↑                    ↑
     |                             |                    |
Data Pipeline                Model Registry         NMS/Filtering

3. data

Data Collection

  • Public datasets: COCO, Pascal VOC, OpenImages

  • Custom collected data for specific use cases

  • Synthetic data generation for rare cases (especially weapons)

Data Storage

  • Raw images: MinIO object storage

  • Annotations: MongoDB (flexible schema for different annotation formats)

  • Features: Vector database (FAISS/Milvus)

Data Pipeline

  • Preprocessing

    • Resize: 640x640 or 1024x1024 based on model

    • Normalization: mean subtraction, scaling to [0,1]

    • Augmentation

      • Geometric: rotation, flip, scale

      • Photometric: brightness, contrast, noise

      • Mosaic augmentation for small objects

      • Mixup for regularization

  • Annotation

    • Bounding box format: [x_center, y_center, width, height]

    • Class labels

    • Quality checks: IOU overlaps, size constraints

4. model

Cloud Deployment:

  • Primary: YOLOv7 or YOLOv8

    • Better accuracy-speed trade-off

    • Strong multi-scale detection

    • Built-in data augmentation

Edge Deployment:

  • Primary: YOLOv8-nano or SSD-MobileNetV3

    • Optimized for mobile/edge

    • Reduced parameter count

    • TensorRT/ONNX compatible

two-stage: region proposal and object classification

  • generates a set of potential object bounding boxes

  • takes the proposed regions from the RPN and classifies them into different object categories

one-stage

  • perform both region proposal and object classification in a single step

nms

5. evaluation

Primary Metrics:

  • mAP@0.5: Overall detection performance

  • mAP@0.5:0.95: Stricter evaluation

    • AP: avg. across various IOU thresholds

  • FPS: Runtime performance

  • Per-class AP for monitoring class-wise performance

Secondary Metrics:

  • Precision-Recall curves

    • Precision based on IOU threshold

  • F1 score at different confidence thresholds

  • Average inference time

6. deploy & serving

  • batch service or online service

7. Monitoring & maintenance

monitoring

  • Data drift detection

  • A/B testing framework

  • Regular model retraining pipeline

  • Performance optimization based on real-world feedback

8. 问答

  • 过杀和漏检：基于遗传算法的帕累托优化

Reference

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