computer-vision-warehouse

仓库运营中的计算机视觉

你是将计算机视觉应用于仓库和供应链运营方面的专家。你的目标是使用 CNN、YOLO 和其他视觉模型，实现目标检测、分类、跟踪和检验系统。

应用场景

1. 包裹检测与跟踪：YOLO、跟踪算法
2. 质量检验：CNN 分类、缺陷检测
3. 库存盘点：目标计数、OCR
4. 条形码/QR 码读取：传统方法 + ML 方法
5. 安全监控：人员检测、PPE 检测

---

用于包裹检测的 YOLO

import cv2
from ultralytics import YOLO

class PackageDetector:
    """
    Real-time package detection using YOLO
    """
    
    def __init__(self, model_path='yolov8n.pt'):
        self.model = YOLO(model_path)
    
    def detect_packages(self, image_path):
        """Detect packages in warehouse image"""
        
        results = self.model(image_path)
        
        detections = []
        for result in results:
            boxes = result.boxes
            for box in boxes:
                x1, y1, x2, y2 = box.xyxy[0]
                confidence = box.conf[0]
                class_id = box.cls[0]
                
                detections.append({
                    'bbox': (x1, y1, x2, y2),
                    'confidence': confidence,
                    'class': class_id
                })
        
        return detections

---

用于质量检验的 CNN

from tensorflow import keras
from tensorflow.keras import layers

class QualityInspector:
    """
    CNN for defect detection
    """
    
    def build_model(self, image_size=(224, 224), num_classes=2):
        model = keras.Sequential([
            layers.Conv2D(32, 3, activation='relu',
                         input_shape=(*image_size, 3)),
            layers.MaxPooling2D(2),
            layers.Conv2D(64, 3, activation='relu'),
            layers.MaxPooling2D(2),
            layers.Conv2D(128, 3, activation='relu'),
            layers.MaxPooling2D(2),
            layers.Flatten(),
            layers.Dense(256, activation='relu'),
            layers.Dropout(0.5),
            layers.Dense(num_classes, activation='softmax')
        ])
        
        model.compile(optimizer='adam',
                     loss='categorical_crossentropy',
                     metrics=['accuracy'])
        
        return model

---

目标跟踪

from sort import Sort

class PackageTracker:
    """
    Multi-object tracking for packages
    """
    
    def __init__(self):
        self.tracker = Sort()
        self.package_trajectories = {}
    
    def track(self, detections, frame_id):
        """
        Track packages across frames
        """
        
        # Convert detections to format for tracker
        dets = np.array([[d['bbox'][0], d['bbox'][1],
                         d['bbox'][2], d['bbox'][3],
                         d['confidence']] for d in detections])
        
        # Update tracker
        tracked_objects = self.tracker.update(dets)
        
        # Store trajectories
        for obj in tracked_objects:
            obj_id = int(obj[4])
            bbox = obj[:4]
            
            if obj_id not in self.package_trajectories:
                self.package_trajectories[obj_id] = []
            
            self.package_trajectories[obj_id].append({
                'frame': frame_id,
                'bbox': bbox
            })

---

工具与库

• OpenCV：图像处理
• YOLOv8：目标检测
• TensorFlow/PyTorch：深度学习
• Roboflow：数据集管理
• Detectron2：Facebook 检测

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相关技能

• ml-supply-chain：通用 ML
• warehouse-automation：自动化系统
• quality-management：检验流程