Implementation and integration of image processing blocks in a real-time bottle classification system.

Area of Science:

• Environmental Science
• Computer Engineering
• Materials Science

Background:

• Waste pollution from large volumes of materials like plastic and glass poses significant environmental challenges.
• Recycling is a practical solution, yet its implementation, particularly for bottle classification, faces hurdles in underdeveloped regions.
• Existing bottle classification systems lack high-performance, real-time capabilities.

Purpose of the Study:

• To propose and implement a high-performance real-time hardware architecture for bottle classification.
• To develop a system capable of processing input images of bottles and outputting their color.
• To address the limitations of current bottle recycling infrastructure through advanced technology.

Main Methods:

• A novel hardware architecture was designed for real-time bottle classification.
• The architecture was implemented on a Spartan-6 Field Programmable Gate Array (FPGA) using a Hardware Description Language.
• Dedicated image processing blocks were integrated into a compact and tunable system and tested in a simulated environment.

Main Results:

• The implemented system achieves a maximum processing rate of 60 frames per second.
• The architecture supports various input resolutions, from 1080p up to 8K.
• The system demonstrates high performance in classifying bottle colors in real-time.

Conclusions:

• The developed FPGA-based architecture offers a high-performance solution for real-time bottle classification.
• This system can significantly enhance the efficiency of recycling processes, especially where current methods are unfeasible.
• The compact and tunable design provides flexibility for integration into existing or new waste management systems.