Side Profile Tires Dataset

Data Collection and Labeling Process:

• Manual Labeling: Every tire in the dataset has been individually labeled to guarantee that the annotations are highly precise, significantly reducing the margin of error in model training.
• Annotation Format: YOLO v5 PyTorch format, a highly efficient and widely used format for real-time object detection systems.

Pre-processing Applied:

• Auto-orientation: Pixel data has been automatically oriented, and EXIF orientation metadata has been stripped to ensure uniformity across all images, eliminating issues related to image orientation during processing.
• Resizing: All images have been resized to 416×416 pixels using stretching to maintain compatibility with common object detection frameworks like YOLO. This resizing standardizes the image input size while preserving visual integrity.

Applications:

• Automotive Industry: This dataset is suitable for automotive-focused AI models, including tire quality assessment, tread pattern recognition, and autonomous vehicle systems.
• Surveillance and Security: Use cases in monitoring systems where identifying tires is crucial for vehicle recognition in parking lots or traffic management systems.
• Manufacturing and Quality Control: Can be used in tire manufacturing processes to automate defect detection and classification.

Dataset Composition:

• Number of Images: [Add specific number]
• File Format: JPEG/PNG
• Annotation Format: YOLO v5 PyTorch
• Image Size: 416×416 (standardized across all images)