Coffee Green Bean Defect Detection Method Based on an Improved YOLOv8 Model

Abstract

This research is aimed at addressing the significant challenges of detecting and classifying green coffee beans, with a particular focus on identifying defective coffee beans—an important task for improving coffee quality and market value. The main challenge is to accurately detect small and visually subtle defects in coffee beans in real-world production environments with a large number of beans, varying lighting conditions, and complex backgrounds. To address these challenges, we propose a YOLOv8n-based object detection model that employs several innovative strategies aimed at improving detection performance and robustness.

Our research includes the introduction of WIoUv3 and the development of the Atn-C3Ghost module, which integrates the ECA mechanism with the C3Ghost module to refine the feature extraction and improve the accuracy of the model.

In order to validate the effectiveness of our proposed method, we conducted comprehensive comparison and ablation experiments. In addition, we compared the C3Ghost structure in combination with various attentional mechanisms to determine their impact on the model’s detection ability. We also conducted ablation studies to evaluate the respective contributions of WIoUv3, ECA, and C3Ghost to overall model performance.

The experimental results show that the YOLOv8n-based model enhanced with WIoUv3, ECA, and C3Ghost achieves an accuracy of 99.0% in detecting green coffee beans, which is significantly better than other YOLO models. This study not only provides a practical solution for green coffee bean detection but also provides a valuable framework for addressing similar challenges in other small object detection tasks.