Title : Performance Evaluation of an Object Detection Model Using Drone Imagery in Urban Areas for Semi-Automatic Artificial Intelligence Dataset Construction

Objectives
Evaluate the performance of object detection models using drone-captured images in urban areas.
Develop semi-automated AI datasets through object detection to create accurate training data from drone imagery.

Strategies and Methods
High-Resolution Imagery: Collect aerial images from drones in urban environments.
Deep Learning Models: Design object detection models based on deep learning to identify and categorize various objects.
Dataset Creation: Build AI training datasets and assess their quality for broader AI applications.
Model Optimization: Apply enhanced algorithms to improve object detection for elements like buildings, vehicles, and people.

Applications
Create accurate datasets for training AI models capable of automated object detection and classification.
Apply models to urban management tasks like traffic monitoring and environmental change detection.
Analyze urban environments and detect specific features for further assessments.
Extend applications to other industries, such as agriculture, disaster management, and mapping.

Models and Algorithms
Convolutional Neural Networks (CNNs): For identifying and classifying objects in drone imagery.
Object Detection Algorithms: Use YOLO (You Only Look Once) or Faster R-CNN for efficient and accurate detection.
Enhanced Techniques: Apply supervised learning and reinforcement methods to boost model accuracy.
Performance Metrics: Use precision, recall, and F1-score to evaluate detection accuracy.

Results
Drone imagery effectively aids in creating AI datasets for object detection in urban settings.
Object detection models demonstrated high accuracy in identifying various urban elements, aiding in applications like city monitoring and planning.

Challenges and Limitations
Image quality issues due to varying weather and lighting conditions.
Complexity in detecting objects within dense and diverse urban environments.
High computational demands for training and evaluating deep learning models.
Time-consuming and labor-intensive labeling of data for reliable dataset creation.

Conclusion
Drone-based imagery is a valuable tool for object detection in urban areas, enabling the creation of accurate AI datasets. These datasets enhance AI models' ability to recognize and classify objects efficiently, benefiting urban management and other industries.

Future Work
Develop advanced models for detecting objects under complex environmental conditions.
Improve dataset quality using higher-resolution images and advanced labeling techniques.
Optimize algorithms to reduce computational needs and increase detection speed.
Incorporate multi-spectral images to enhance model performance under various lighting and environmental conditions.