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

• 仪表识别

• 安全帽

• 万字长文细说工业缺陷检测 - 皮特潘的文章 - 知乎

• https://github.com/Charmve/Surface-Defect-Detection

• https://github.com/Sharpiless/Yolov5-Flask-VUE

• Meta (Facebook) Machine Learning Case Study: Illegal Items Detection

• 通用目标检测开源框架YOLOv6在美团的量化部署实战

• 基于多模态信息抽取的菜品知识图谱构建

• 美团视觉GPU推理服务部署架构优化实践

• Binance P2P’s Invisible Guardians: Using Computer Vision to Detect Fraud