Smart Waste Classification – IT and Computer Engineering Guide
1. Project Overview
Objective: Develop an intelligent system to classify trash
into categories like recyclable, organic, and non-recyclable using image data.
Scope: Use deep learning techniques to analyze images of waste for automated
sorting, promoting better waste management.
2. Prerequisites
Knowledge: Basics of Python programming, convolutional
neural networks (CNNs), and image preprocessing.
Tools: Python, TensorFlow/Keras, OpenCV, and Matplotlib.
Dataset: Publicly available waste classification datasets or custom datasets.
3. Project Workflow
- Dataset Collection: Obtain or create a dataset of labeled images of different waste types.
- Data Preprocessing: Resize, normalize, and augment the image data for training.
- Model Development: Build a convolutional neural network (CNN) for image classification.
- Model Evaluation: Evaluate the model using accuracy, precision, recall, and confusion matrix.
- Deployment: Deploy the model in a web or mobile application for real-time waste classification.
4. Technical Implementation
Step 1: Import Libraries
import os
import cv2
import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense,
Dropout
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from sklearn.metrics import classification_report, confusion_matrix
import matplotlib.pyplot as plt
Step 2: Load and Preprocess Data
# Set paths
data_dir = 'dataset/'
categories = ['Recyclable', 'Organic', 'NonRecyclable']
# Prepare data
data = []
for category in categories:
path = os.path.join(data_dir,
category)
class_num =
categories.index(category)
for img in os.listdir(path):
try:
img_array =
cv2.imread(os.path.join(path, img), cv2.IMREAD_COLOR)
resized_img =
cv2.resize(img_array, (150, 150))
data.append([resized_img,
class_num])
except Exception as e:
pass
# Split data
X, y = zip(*data)
X = np.array(X) / 255.0
y = np.array(y)
Step 3: Train the Model
# Build CNN model
model = Sequential([
Conv2D(32, (3, 3), activation='relu',
input_shape=(150, 150, 3)),
MaxPooling2D(2, 2),
Conv2D(64, (3, 3),
activation='relu'),
MaxPooling2D(2, 2),
Flatten(),
Dense(128, activation='relu'),
Dropout(0.5),
Dense(len(categories),
activation='softmax')
])
# Compile model
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
# Train model
history = model.fit(X, y, epochs=10, validation_split=0.2, batch_size=32)
Step 4: Evaluate the Model
# Predictions
predictions = model.predict(X)
y_pred = np.argmax(predictions, axis=1)
# Metrics
print(classification_report(y, y_pred, target_names=categories))
conf_matrix = confusion_matrix(y, y_pred)
plt.imshow(conf_matrix, cmap='coolwarm', interpolation='none')
plt.title('Confusion Matrix')
plt.show()
Step 5: Save and Deploy the Model
# Save the model
model.save('waste_classification_model.h5')
# Deploy in an application using Flask or similar frameworks
5. Results and Insights
Evaluate the model's performance on waste classification and highlight its effectiveness in sorting trash accurately.
6. Challenges and Mitigation
Data Imbalance: Address class imbalance through data
augmentation.
Image Variability: Use robust preprocessing techniques to handle variations in
lighting, angles, and quality.
7. Future Enhancements
Enhance the dataset with more categories and real-world
images.
Incorporate object detection to identify and classify multiple items in a
single image.
8. Conclusion
The Smart Waste Classification project demonstrates the use of deep learning to improve waste sorting, contributing to sustainable environmental practices.