Computer Vision YOLO Team Productivity — ใช้

Computer Vision YOLO Productivity

Computer Vision YOLO Team Productivity Object Detection Real-time Automation Inventory Quality Safety People Counting Production Deploy

เนื้อหาเกี่ยวข้อง — อ่านต่อ: fear and greed index วันนี้

Use Case	Before YOLO	After YOLO	Improvement
Inventory Count	2 ชม./วัน Manual	5 นาที Auto	96% เร็วขึ้น
Quality Inspection	Human ตรวจ 200 ชิ้น/ชม.	Auto 2000+ ชิ้น/ชม.	10x Throughput
Safety PPE Check	Supervisor ตรวจ Manual	Real-time Camera Alert	24/7 Monitoring
People Counting	Manual Count	Real-time Auto Count	100% Accuracy
Parking	ดูจอ CCTV	Auto Detect ว่าง/ไม่ว่าง	Real-time Dashboard

YOLO Quick Start

# === YOLO Quick Start ===



# pip install ultralytics

#

# from ultralytics import YOLO

#

# # Load pretrained model

# model = YOLO('yolov8n.pt')  # nano (fastest)

# # model = YOLO('yolov8s.pt')  # small

# # model = YOLO('yolov8m.pt')  # medium

#

# # Inference on image

# results = model('image.jpg')

# results[0].show()  # Display results

# results[0].save('output.jpg')  # Save results

#

# # Inference on video

# results = model('video.mp4', stream=True)

# for r in results:

#     boxes = r.boxes  # Bounding boxes

#     for box in boxes:

#         cls = int(box.cls[0])

#         conf = float(box.conf[0])

#         print(f"Class: {model.names[cls]} Conf: {conf:.2f}")

#

# # Webcam real-time

# results = model(source=0, show=True, stream=True)

# for r in results:

#     pass  # Process frames

#

# # Train custom model

# model = YOLO('yolov8n.pt')

# model.train(data='dataset.yaml', epochs=100, imgsz=640, batch=16)

# # dataset.yaml:

# # path: ./dataset

# # train: images/train

# # val: images/val

# # names: {0: 'helmet', 1: 'no_helmet', 2: 'vest', 3: 'no_vest'}



from dataclasses import dataclass



@dataclass

class YOLOModel:

    name: str

    params: str

    map50: str

    speed_gpu: str

    use_case: str



models = [

    YOLOModel("YOLOv8n (Nano)",

        "3.2M params",

        "mAP50: 37.3%",

        "1.2ms (A100 GPU)",

        "Edge Mobile Real-time สำคัญ"),

    YOLOModel("YOLOv8s (Small)",

        "11.2M params",

        "mAP50: 44.9%",

        "1.5ms",

        "สมดุล Speed/Accuracy"),

    YOLOModel("YOLOv8m (Medium)",

        "25.9M params",

        "mAP50: 50.2%",

        "2.7ms",

        "แม่นกว่า GPU พอ"),

    YOLOModel("YOLOv8l (Large)",

        "43.7M params",

        "mAP50: 52.9%",

        "3.9ms",

        "ต้องการความแม่นยำสูง"),

    YOLOModel("YOLOv8x (XLarge)",

        "68.2M params",

        "mAP50: 53.9%",

        "5.6ms",

        "แม่นสุด ไม่กังวล Speed"),

]



print("=== YOLO Models ===")

for m in models:

    print(f"  [{m.name}] {m.params}")

    print(f"    Accuracy: {m.map50} | Speed: {m.speed_gpu}")

    print(f"    Use: {m.use_case}")

Team Automation

# === Team Productivity Automation ===



@dataclass

class AutomationUseCase:

    use_case: str

    yolo_task: str

    dataset: str

    roi: str

    team_impact: str



automations = [

    AutomationUseCase("Inventory Counting",

        "Detection (นับ Bounding Box ต่อ Class)",

        "ถ่ายภาพสินค้า 500+ ภาพ Label ด้วย Roboflow",

        "ลดเวลา 96% คืนทุน 2 เดือน",

        "Warehouse Team ลดงาน Manual นับของ 2 ชม./วัน"),

    AutomationUseCase("Quality Inspection",

        "Detection + Segmentation (ตรวจ Defect)",

        "ภาพ OK/NG 1000+ ภาพ Label Defect Region",

        "ลด Error 90% Throughput 10x",

        "QC Team ตรวจ 2000+ ชิ้น/ชม. แทน 200 ชิ้น"),

    AutomationUseCase("Safety PPE Detection",

        "Detection (Helmet Vest Glasses Boot)",

        "ภาพคนงาน 2000+ ภาพ Label PPE Items",

        "ลดอุบัติเหตุ 50% ตรวจ 24/7",

        "Safety Team ไม่ต้องตรวจ Manual ได้ Alert Real-time"),

    AutomationUseCase("Meeting Room Occupancy",

        "Detection + Counting (นับคน)",

        "COCO Pretrained (Person Class) ไม่ต้อง Train ใหม่",

        "ประหยัดค่าไฟ HVAC 15-25%",

        "Facility Team ปรับ HVAC อัตโนมัติ ตาม Occupancy"),

    AutomationUseCase("Document Region Detection",

        "Detection (ตรวจ Text Table Image Region)",

        "ภาพเอกสาร 500+ ภาพ Label Region Types",

        "ลดเวลา Data Entry 70%",

        "Admin Team ลดงาน Manual Data Entry"),

]



print("=== Automation Use Cases ===")

for a in automations:

    print(f"\n  [{a.use_case}] Task: {a.yolo_task}")

    print(f"    Dataset: {a.dataset}")

    print(f"    ROI: {a.roi}")

    print(f"    Impact: {a.team_impact}")

Production Deployment

# === Production Deployment ===



# Export for Production

# from ultralytics import YOLO

# model = YOLO('best.pt')  # Your trained model

# model.export(format='engine')  # TensorRT (NVIDIA)

# model.export(format='onnx')    # ONNX (Cross-platform)

# model.export(format='coreml')  # CoreML (Apple)

# model.export(format='tflite')  # TFLite (Mobile/Edge)



# Docker Deployment

# docker run --gpus all -v ./models:/models -p 8000:8000 \

#   ultralytics/ultralytics python serve.py



@dataclass

class DeployOption:

    platform: str

    technology: str

    fps: str

    cost: str

    best_for: str



options = [

    DeployOption("Docker + GPU Server",

        "Docker + NVIDIA GPU + TensorRT",

        "60-100+ FPS",

        "Server Cost (GPU $500-2000/เดือน)",

        "Production Server Multi-camera High FPS"),

    DeployOption("Kubernetes + Triton",

        "K8s + Triton Inference Server + GPU Pool",

        "Scalable (100+ FPS per Pod)",

        "K8s Cluster + GPU Nodes",

        "Enterprise Scale Multi-model Multi-tenant"),

    DeployOption("Edge (Jetson)",

        "NVIDIA Jetson Orin Nano + TensorRT",

        "30-60 FPS (YOLOv8n)",

        "Hardware $5,000-15,000 บาท/ตัว",

        "Factory Floor Retail Store Edge Processing"),

    DeployOption("Cloud (SageMaker)",

        "AWS SageMaker / GCP Vertex AI",

        "Auto-scale",

        "Pay-per-use (g4dn.xlarge $0.526/hr)",

        "Variable Load Batch Processing API Service"),

    DeployOption("Browser (ONNX.js)",

        "ONNX Runtime Web / TensorFlow.js",

        "5-15 FPS (CPU) 30+ FPS (WebGPU)",

        "ฟรี (Client-side)",

        "Demo Prototype Low-volume Client-side"),

]



print("=== Deployment Options ===")

for o in options:

    print(f"\n  [{o.platform}] {o.technology}")

    print(f"    FPS: {o.fps} | Cost: {o.cost}")

    print(f"    Best for: {o.best_for}")