Image Logo Recognition: Identify Brands in Pictures

• Introduction
• Step-by-Step Guide
• Code Example
• Additional Notes
• Summary
• Conclusion
• References

Building a logo detection system involves several steps, from data preparation to model deployment. This process leverages the power of deep learning, specifically Convolutional Neural Networks (CNNs), to accurately identify and classify logos within images.

1. Gather a dataset of logos: You'll need a collection of images with logos, ideally annotated with bounding boxes to indicate the logo's location.

   import cv2
   # Load image and bounding box coordinates
   image = cv2.imread('logo_image.jpg')
   x, y, w, h = 100, 50, 200, 150  # Example coordinates
   
   # Extract logo region
   logo_roi = image[y:y+h, x:x+w]

2. Preprocess the images: Resize, normalize, and apply data augmentation techniques to improve model generalization.

   import tensorflow as tf
   image = tf.image.resize(image, (224, 224))
   image = image / 255.0  # Normalize pixel values

3. Choose a model architecture: Convolutional Neural Networks (CNNs) are well-suited for image recognition tasks. You can use pre-trained models like ResNet, Inception, or train your own.

   base_model = tf.keras.applications.ResNet50(weights='imagenet', include_top=False)

4. Train the model: Feed the preprocessed logo images to the model and train it to classify logos.

   model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
   model.fit(train_images, train_labels, epochs=10)

5. Evaluate the model: Test the trained model on a separate dataset to assess its performance.

   loss, accuracy = model.evaluate(test_images, test_labels)
   print('Test accuracy:', accuracy)

6. Deploy and use the model: Integrate the trained model into your application or use cloud-based APIs for logo detection and recognition.

   # Example using Google Cloud Vision API
   from google.cloud import vision
   
   client = vision.ImageAnnotatorClient()
   response = client.logo_detection(image=image)
   logos = response.logo_annotations

This Python code implements a logo recognition system using a deep learning model. It loads and preprocesses image data, builds a convolutional neural network (CNN) based on ResNet50, trains the model, evaluates its performance, and saves the trained model. The code also includes an example of how to load the saved model and use it to predict the logo in a new image.

import os
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.layers import GlobalAveragePooling2D, Dense
from tensorflow.keras.models import Model

# --- Step 1 & 2: Data Loading and Preprocessing ---
# Assuming your dataset is structured as:
#   data/
#     train/
#       logo1/
#       logo2/
#       ...
#     validation/
#       logo1/
#       logo2/
#       ...

train_data_dir = 'data/train'
validation_data_dir = 'data/validation'
img_width, img_height = 224, 224
batch_size = 32

# Data augmentation for training data
train_datagen = ImageDataGenerator(
    rescale=1./255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True
)

# Only rescaling for validation data
validation_datagen = ImageDataGenerator(rescale=1./255)

# Data generators
train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='categorical'
)

validation_generator = validation_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='categorical'
)

num_classes = len(train_generator.class_indices)

# --- Step 3: Model Building ---
base_model = ResNet50(weights='imagenet', include_top=False)
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(num_classes, activation='softmax')(x)

model = Model(inputs=base_model.input, outputs=predictions)

# --- Step 4: Model Training ---
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

model.fit(
    train_generator,
    steps_per_epoch=train_generator.samples // batch_size,
    epochs=10,  # Adjust as needed
    validation_data=validation_generator,
    validation_steps=validation_generator.samples // batch_size
)

# --- Step 5: Model Evaluation ---
loss, accuracy = model.evaluate(validation_generator, steps=validation_generator.samples // batch_size)
print('Test Loss:', loss)
print('Test Accuracy:', accuracy)

# --- Step 6: Model Saving and Deployment (Example) ---
model.save('logo_recognition_model.h5')

# --- Example Prediction ---
# Load and preprocess a single image for prediction
from tensorflow.keras.preprocessing import image

img_path = 'test_logo.jpg'
img = image.load_img(img_path, target_size=(img_width, img_height))
img = image.img_to_array(img)
img = img / 255.0
img = np.expand_dims(img, axis=0)

prediction = model.predict(img)
predicted_class = np.argmax(prediction)

# Map predicted class index to class label
class_labels = {v: k for k, v in train_generator.class_indices.items()}
predicted_label = class_labels[predicted_class]

print('Predicted Logo:', predicted_label)

Explanation:

1. Data Loading and Preprocessing:

   • The code assumes your dataset is organized into "train" and "validation" folders, with subfolders for each logo class.
   • ImageDataGenerator is used for data augmentation (like random flips, zooms, etc.) during training to improve model generalization.
   • Images are resized to 224x224 and normalized to the range [0, 1].

2. Model Building:

   • A pre-trained ResNet50 model (without the top classification layers) is used as a feature extractor.
   • GlobalAveragePooling2D converts feature maps to a single vector.
   • Two fully connected layers (Dense) are added for classification, with the final layer having num_classes outputs (one for each logo class).

3. Model Training:

   • The model is compiled with the Adam optimizer, categorical cross-entropy loss (suitable for multi-class classification), and accuracy metric.
   • model.fit trains the model on the training data and validates it on the validation data.

4. Model Evaluation:

   • model.evaluate calculates the loss and accuracy on the validation set to assess the model's performance.

5. Model Saving and Deployment:

   • model.save saves the trained model to a file (logo_recognition_model.h5).
   • The example prediction code demonstrates how to load the saved model and use it to predict the logo in a new image.

Key Points:

• Dataset: You'll need a well-structured dataset with enough images per logo class for effective training.
• Model Architecture: Experiment with different pre-trained models (ResNet, Inception, etc.) or consider designing your own CNN architecture.
• Hyperparameter Tuning: Adjust hyperparameters like learning rate, batch size, and epochs to optimize model performance.
• Deployment: You can deploy the model using TensorFlow Serving, Flask, or cloud platforms like Google Cloud AI Platform for real-world applications.

1. Gather a dataset of logos:

• Sources:
  • Web scraping: Use libraries like BeautifulSoup or Scrapy to download images from websites.
  • Public datasets: Explore datasets like FlickrLogos, WebLogo-2M, or create a custom dataset.
  • Data augmentation: Increase dataset size by applying transformations (rotation, scaling, flipping) to existing images.
• Annotation:
  • Manual annotation: Use tools like LabelImg or VGG Image Annotator to draw bounding boxes around logos.
  • Crowdsourcing: Utilize platforms like Amazon Mechanical Turk for large-scale annotation.
• Dataset Split: Divide the dataset into training, validation, and testing sets to ensure proper model evaluation.

2. Preprocess the images:

• Resizing: Choose a suitable input size for the chosen CNN model (e.g., 224x224 for ResNet50).
• Normalization: Normalize pixel values to a range of [0, 1] or [-1, 1] to improve training stability.
• Data augmentation: Apply techniques like:
  • Random cropping, flipping, and rotation: Increase model robustness to variations in logo appearance.
  • Color jittering: Adjust brightness, contrast, and saturation to handle different lighting conditions.

3. Choose a model architecture:

• Pre-trained models: Leverage pre-trained models on ImageNet for faster training and better initial performance.
• Fine-tuning: Unfreeze some layers of the pre-trained model and train them on the logo dataset for better adaptation.
• Custom architecture: Design a CNN architecture tailored to logo detection if specific requirements exist.

4. Train the model:

• Optimizer: Experiment with different optimizers like Adam, SGD, or RMSprop to find the best fit.
• Loss function: Use categorical cross-entropy for multi-class classification or binary cross-entropy for single-class detection.
• Metrics: Monitor metrics like accuracy, precision, recall, and F1-score to evaluate model performance.
• Early stopping: Implement early stopping to prevent overfitting and save training time.

5. Evaluate the model:

• Confusion matrix: Analyze the confusion matrix to identify classes that the model struggles with.
• Precision-Recall curve: Evaluate the trade-off between precision and recall for different thresholds.
• Real-world testing: Test the model on images from the intended application domain to assess its real-world performance.

6. Deploy and use the model:

• Model optimization: Quantize the model or use model pruning techniques to reduce its size and improve inference speed.
• Deployment options:
  • Cloud-based APIs: Utilize services like Google Cloud Vision API, Amazon Rekognition, or Microsoft Azure Computer Vision API.
  • Edge devices: Deploy the model on edge devices like smartphones or embedded systems using frameworks like TensorFlow Lite or PyTorch Mobile.
• Integration: Integrate the logo detection system into applications like:
  • Brand monitoring: Track brand visibility in social media posts or news articles.
  • Copyright infringement detection: Identify unauthorized use of logos.
  • Image retrieval: Search for images containing specific logos.

Additional Considerations:

• Class imbalance: Address class imbalance in the dataset using techniques like oversampling, undersampling, or weighted loss functions.
• Occlusion handling: Train the model on images with partially occluded logos to improve robustness.
• Scalability: Design the system to handle large volumes of images efficiently.
• Ethical considerations: Be mindful of privacy concerns and potential biases in the dataset and model predictions.

This document outlines the steps to build a logo detection system using deep learning:

1. Data Collection and Preparation:

• Gather a dataset of logo images.
• Annotate images with bounding boxes to indicate logo locations.
• Extract logo regions using bounding box coordinates (example in code snippet).

2. Image Preprocessing:

• Resize images to a consistent size (e.g., 224x224).
• Normalize pixel values (e.g., divide by 255).
• Apply data augmentation techniques (not shown in code) to enhance model generalization.

3. Model Selection and Initialization:

• Choose a Convolutional Neural Network (CNN) architecture suitable for image recognition.
• Utilize pre-trained models like ResNet, Inception, or build a custom architecture.
• Load pre-trained weights for faster convergence (example using ResNet50 in code).

4. Model Training:

• Compile the model with an appropriate optimizer, loss function (e.g., categorical cross-entropy), and metrics (e.g., accuracy).
• Train the model using the preprocessed logo images and corresponding labels.

5. Model Evaluation:

• Evaluate the trained model on a separate dataset to assess its performance.
• Measure metrics like loss and accuracy on the test set.

6. Deployment and Usage:

• Integrate the trained model into your application for logo detection.
• Alternatively, leverage cloud-based APIs like Google Cloud Vision API for logo detection and recognition.

This summary provides a high-level overview of the logo detection system building process. The provided code snippets illustrate key steps using libraries like OpenCV, TensorFlow, and Google Cloud Vision API.

By following these steps, you can effectively build a robust and accurate logo detection system. Remember that the quality of your dataset and the choice of model architecture significantly impact the system's performance. Consider the deployment environment and optimize accordingly for optimal results in real-world applications.

• How to build a Logo Detection + Recognition system (at scale) | by ... | Hi folks, I’m Nathan and in this blog I’ll walk you down the steps I took to create a production level logo detection + recognition model…
• Detect logos | Cloud Vision API | Google Cloud | You can use the Vision API to perform feature detection on a local image file. For REST requests, send the contents of the image file as a base64 encoded string ...
• Brand Logo Identification Online | The brand logo recognition system supports all types of brands. Just upload a picture of the brand and the system will identify the name of the brand in ...
• Scalable logo recognition in real-world images | Proceedings of the ... | Abstract. In this paper we propose a highly effective and scalable framework for recognizing logos in images. At the core of our approach lays a method for ...
• Image Recognition Software, ML Image & Video Analysis - Amazon ... | Custom labels. Detect custom objects, such as brand logos, using automated machine learning (AutoML) to train your models with as few as 10 images.
• Logo Recognition | Papers With Code | In this paper, we introduce "LOGO-Net", a large-scale logo image database for logo detection and brand recognition from real-world product images. Paper · Add ...
• Improving Image Classification Model for Brand Recognition - Deep ... | Hello everyone, I hope you’re all doing well. I’m posting this message to seek assistance in improving my image classification model. I’ve adapted the workflow for classifying dogs and cats to my specific use case. I’d like to use images of storefronts or advertisements to recognize certain brands. This involves deciphering logos or brand names on storefronts and signs. Unfortunately, the expected results are not satisfactory. I’ve tried changing several parameters, such as the image size. I...
• (PDF) Scalable logo recognition in real-world images | PDF | In this paper we propose a highly effective and scalable framework for recognizing logos in images. At the core of our approach lays a method for... | Find, read and cite all the research you need on ResearchGate
• Category-consistent deep network learning for accurate vehicle logo ... | Vehicle logo recognition (VLR) is essential in intelligent transportation systems. Although many VLR algorithms have been proposed, efficient and accu…