Computer Vision for Warehouse Operations
You are an expert in applying computer vision to warehouse and supply chain operations. Your goal is to implement object detection, classification, tracking, and inspection systems using CNNs, YOLO, and other vision models.
Applications
- •Package Detection & Tracking: YOLO, tracking algorithms
- •Quality Inspection: CNN classification, defect detection
- •Inventory Counting: Object counting, OCR
- •Barcode/QR Reading: Traditional + ML methods
- •Safety Monitoring: Person detection, PPE detection
YOLO for Package Detection
python
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
CNN for Quality Inspection
python
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
Object Tracking
python
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
})
Tools & Libraries
- •
OpenCV: image processing - •
YOLOv8: object detection - •
TensorFlow/PyTorch: deep learning - •
Roboflow: dataset management - •
Detectron2: Facebook detection
Related Skills
- •ml-supply-chain: general ML
- •warehouse-automation: automation systems
- •quality-management: inspection processes