# Image Similarity Judgment Method for Waste Printed Circuit Boards

## TL;DR

This paper introduces an image-based method to automatically classify waste printed circuit boards, improving recycling efficiency and accuracy.

## Contribution

A novel image similarity algorithm for WPCB classification using visual features and weighted contributions is proposed.

## Key findings

- The proposed method achieves 88.0% accuracy in WPCB classification.
- Key features like hue value and structural complexity improve similarity evaluation.
- The approach outperforms self-supervised contrastive learning methods.

## Abstract

Waste printed circuit boards (WPCBs) contain valuable metals such as gold, palladium, and silver, which are typically recovered through non-ferrous metal smelting. Currently, WPCBs are manually classified by workers, who visually compare board colors and component layouts with previously processed boards. This approach is time-consuming and prone to human error. To address these limitations, we propose an image-based algorithm for automated WPCB similarity assessment. The method extracts visual features from board images and computes similarity scores, incorporating classification strategies based on board-specific characteristics. Key features identified as effective for similarity evaluation include the hue value, coefficient of variation in terminal regions, number of line elements in terminal regions, structural complexity, and number of integrated circuits. Weighted feature contributions further improve accuracy. Our experimental results demonstrate that the proposed approach achieves 88.0% accuracy for the targeted PCB types, outperforming a comparative self-supervised contrastive learning method. This image-driven solution can significantly streamline WPCB recycling by reducing reliance on manual inspection and improving operational efficiency.