The Cars Drone Detection Dataset is a comprehensive collection of images designed for the task of detecting cars in aerial views captured by drones. The dataset comprises high-resolution images (512×512 pixels, 3 channels) that showcase cars in varying quantities, angles, and qualities. Each image is annotated with the outlines of cars using the Pascal VOC format, providing precise localization for detection tasks.
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Key Features
High-Resolution Images: Each image is 512×512 pixels with 3 color channels, ensuring detailed visual information.
Diverse Perspectives: The dataset includes cars captured from different angles and distances, simulating real-world scenarios.
Quality Variations: Images exhibit varying degrees of quality, challenging detection models to perform well under different conditions.
Precise Annotations: Car outlines are marked using the Pascal VOC format, facilitating accurate object detection and localization tasks.
Applications
Detection Tasks: Ideal for practicing car detection using aerial imagery.
Image Augmentation: Provides a robust dataset for experimenting with various augmentation techniques.
Model Training: Suitable for training and validating detection models, including those based on SSD (Single Shot Multibox Detector) architectures.
Included Resources
Annotated Images: A set of images with car outlines marked for detection.
Data Processing Notebook: An attached notebook demonstrating data preprocessing steps, including train-test split, augmentation, and implementation of the SSD300 detection network with a complete learning cycle.
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