Trash Detection

Project overview

Agricultural field images were annotated for a computer vision dataset focused on detecting trash and waste objects in agricultural environments. Visible trash items within crop fields were identified and accurate bounding box annotations created around them.

The dataset was prepared as training data for an AI-based object detection model designed to automatically detect unwanted waste in agricultural fields.

Work performed

• Reviewed the agricultural field images before annotation.
• Identified visible trash and waste objects among the crops and soil.
• Created accurate bounding boxes around individual trash objects.
• Maintained consistent labelling using the Trash class.
• Small and partially visible waste objects were annotated with particular care.
• Carried out quality checks and corrected missed or inaccurate annotations.
• Ensured the annotations suited object detection model training.

What made it hard

• Detecting small trash objects within agricultural fields.
• Distinguishing trash from soil, crops, weeds and natural vegetation.
• Partially hidden objects among plants were easy to overlook.
• Trash varied widely in shape, size and appearance.
• Complex field backgrounds and changing image conditions added difficulty.
• Keeping bounding boxes tight and accurate around very small objects.

Result

A high-quality agricultural trash detection dataset was completed with accurate bounding box annotations. The dataset was prepared for training a computer vision model capable of automatically detecting waste objects in agricultural field environments.

Final deliverable

Delivered accurately annotated agricultural field images with bounding boxes around visible trash objects, with consistent labelling and annotation quality maintained throughout the dataset. The final dataset matched the project requirements and was ready for AI model training and agricultural waste detection applications.