Image Annotation
Refining Recyclable Material Classification with AI-Powered Image Annotation
The Challenge
Our client, a progressive company in the recycling sector, sought to enhance its computer vision systems for automated sorting of recyclable materials. The goal was to achieve high-precision image annotation to help their AI model accurately identify a wide range of materials—including glass, plastic, paper, and metals—under complex conditions. This challenge required both a deep understanding of the materials and an ability to differentiate subtle visual distinctions, especially when visual differences were minimal. The client needed a partner with the capability to manage high-level accuracy and adapt to a complex visual environment, so they turned to DataForce for expertise in detailed, consistent image annotation.
• • • •The Solution• • • •
DataForce developed a custom annotation solution that combined visual reference technologies and expert human intervention. The team used AI-enhanced tools such as Google Lens for rapid material identification and the Segment Anything Model (SAM) for high-density image segmentation. This approach was integrated with a team of skilled human annotators to ensure precision even in challenging scenarios, such as overlapping materials.
To maintain consistency and quality, DataForce implemented a rigorous quality assurance process, which included:
- A multi-annotator review workflow to minimize errors.
- A disambiguation review step to ensure annotation accuracy.
The team also established a real-time feedback channel, enabling rapid response to client changes and evolving task guidelines.
The Results:
By leveraging DataForce’s tailored annotation processes and agile project management, the client achieved:
- Enhanced Model Accuracy: Improved identification of recyclable materials.
- Greater Sorting Efficiency: Streamlined processes for automated recycling.
- Scalable Technology: A robust foundation for future advancements in recycling automation.
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