Creating an aircraft detection system is an ambitious project that blends the complexities of computer vision with the intricacies of machine learning. In this blog post, I'll delve into the journey of developing my aircraft detection code, highlighting the strategic decisions, the structure of my GitHub repository, and the lessons learned along the way. While the system has only achieved partial success, the experience has been invaluable in understanding both the potentials and limitations of machine learning models in real-world applications.
Aircraft detection plays a crucial role in various domains, including aviation safety, airport security, and air traffic management. Automating this process not only enhances efficiency but also reduces the likelihood of human error. Inspired by these applications, I embarked on creating an aircraft detection system capable of identifying and classifying military aircraft from images.
For this project, I utilized the Military Aircraft Detection Dataset from Kaggle. This dataset comprises a diverse collection of military aircraft images, providing a solid foundation for training a detection model. However, recognizing the computational constraints and the need for faster training iterations, I opted to use a subset of 1,000 images from the original dataset.
Choosing a smaller dataset offered several advantages:
My GitHub repository for this project is organized to facilitate clarity and ease of navigation. Here's an overview of the key components:
dataset.py: Data Handling and PreprocessingThis script is responsible for loading the dataset, performing necessary preprocessing steps, and preparing the data for training. Key functionalities include:
# Example snippet from dataset.py
import torch
from torchvision import transforms, datasets
def get_data_loaders(batch_size=32):
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
dataset = datasets.ImageFolder(root='data/', transform=transform)
train_size = int(0.8 * len(dataset))
val_size = int(0.1 * len(dataset))
test_size = len(dataset) - train_size - val_size
train_dataset, val_dataset, test_dataset = torch.utils.data.random_split(
dataset, [train_size, val_size, test_size])
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=batch_size, shuffle=False)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False)
return train_loader, val_loader, test_loader
model.py: Defining the Neural Network ArchitectureThis file outlines the architecture of the Convolutional Neural Network (CNN) used for aircraft detection. I experimented with several architectures, ultimately selecting a variant of ResNet due to its proven effectiveness in image recognition tasks.
# Example snippet from model.py
import torch.nn as nn
import torchvision.models as models
def get_model(num_classes):
model = models.resnet18(pretrained=True)
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, num_classes)
return model
train.py: Training the ModelThe training script orchestrates the learning process, handling the forward and backward passes, loss computation, and optimizer updates. Key aspects include:
# Example snippet from train.py
import torch
import torch.optim as optim
from dataset import get_data_loaders
from model import get_model
def train_model():
train_loader, val_loader, _ = get_data_loaders(batch_size=32)
model = get_model(num_classes=5) # Assuming 5 classes
model = model.to('cuda' if torch.cuda.is_available() else 'cpu')
criterion = torch.nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
for epoch in range(10):
model.train()
running_loss = 0.0
for inputs, labels in train_loader:
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
print(f"Epoch {epoch+1}, Loss: {running_loss/len(train_loader)}")
# Add validation logic here
torch.save(model.state_dict(), 'model.pth')
evaluate.py: Assessing Model PerformancePost-training, this script evaluates the model's accuracy, precision, recall, and other relevant metrics on the validation and test datasets. It also generates confusion matrices and other visualizations to understand the model's strengths and weaknesses.
# Example snippet from evaluate.py
import torch
from dataset import get_data_loaders
from model import get_model
from sklearn.metrics import classification_report, confusion_matrix
def evaluate_model():
_, _, test_loader = get_data_loaders(batch_size=32)
model = get_model(num_classes=5)
model.load_state_dict(torch.load('model.pth'))
model = model.to('cuda' if torch.cuda.is_available() else 'cpu')
model.eval()
all_preds = []
all_labels = []
with torch.no_grad():
for inputs, labels in test_loader:
inputs = inputs.to(device)
outputs = model(inputs)
_, preds = torch.max(outputs, 1)
all_preds.extend(preds.cpu().numpy())
all_labels.extend(labels.numpy())
print(classification_report(all_labels, all_preds))
print(confusion_matrix(all_labels, all_preds))
predict.py: Deploying the Model for InferenceThis script facilitates the deployment of the trained model, allowing for real-time or batch predictions on new images. It handles image preprocessing, model inference, and result visualization with bounding boxes around detected aircraft.
# Example snippet from predict.py
import torch
from PIL import Image
from torchvision import transforms
from model import get_model
def predict_image(image_path):
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
image = Image.open(image_path).convert('RGB')
image = transform(image).unsqueeze(0)
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = get_model(num_classes=5)
model.load_state_dict(torch.load('model.pth', map_location=device))
model = model.to(device)
model.eval()
with torch.no_grad():
outputs = model(image.to(device))
_, preds = torch.max(outputs, 1)
return preds.item()
utils.py: Auxiliary FunctionsThis file contains utility functions that support various operations across the project, such as data visualization, performance logging, and other helper methods.
# Example snippet from utils.py
import matplotlib.pyplot as plt
import numpy as np
def plot_confusion_matrix(cm, classes):
plt.figure(figsize=(10,8))
plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
plt.title("Confusion Matrix")
plt.colorbar()
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45)
plt.yticks(tick_marks, classes)
# Normalize the confusion matrix.
cm_normalized = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]
thresh = cm.max() / 2.
for i, j in np.ndindex(cm.shape):
plt.text(j, i, f"{cm[i, j]} ({cm_normalized[i, j]:.2f})",
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.ylabel('True Label')
plt.xlabel('Predicted Label')
plt.tight_layout()
plt.show()
Opting to use a subset of 1,000 images from the Kaggle dataset was a strategic decision aimed at achieving faster training times. This choice allowed for:
While a lean dataset accelerates development, it introduces certain challenges:
During evaluation, the model demonstrated the ability to detect aircraft in images, often getting close to the actual aircraft but not always pinpointing the exact one. This partial success highlighted both the strengths and areas needing improvement in the current approach.
To ensure that the model could detect aircraft, I had to adjust the sensitivity of the detection thresholds. Lowering the sensitivity made the model more permissive in recognizing aircraft, which was a double-edged sword:
Several factors contributed to the model's inability to consistently detect aircraft accurately:
A more extensive and diverse dataset would likely improve the model's ability to detect aircraft accurately across various conditions. Future efforts will focus on expanding the dataset and incorporating more challenging scenarios to enhance robustness.
Finding the right balance between sensitivity and specificity is crucial. Techniques such as adjusting the detection threshold dynamically or employing more sophisticated post-processing methods could help achieve more precise detections without compromising detection rates.
Experimenting with more advanced or specialized architectures, such as YOLO (You Only Look Once) or Faster R-CNN, might offer improved detection capabilities. These models are designed for object detection tasks and could provide better localization of aircraft within images.
Implementing strategies like transfer learning, where the model leverages pre-trained weights from large datasets, could accelerate training and improve performance even with a smaller dataset. Additionally, techniques like cross-validation and hyperparameter optimization can further refine the model's capabilities.
The development of an aircraft detection system using a limited dataset has been a journey of exploration and learning. While the current model exhibits promising detection capabilities, achieving precise and reliable aircraft identification remains a work in progress. The challenges encountered, from managing large datasets to fine-tuning model sensitivity, have provided valuable insights into the complexities of machine learning projects.
As I continue to refine the system—expanding the dataset, experimenting with advanced architectures, and enhancing detection accuracy—I remain optimistic about the potential applications and the impact such a system can have in the aviation sector. This project not only advances my technical skills but also reinforces the importance of strategic planning, iterative development, and continuous learning in the realm of artificial intelligence.