Detection of Coal and Gangue Based on Improved YOLOv8

Abstract

To address the lightweight and real-time issues of coal sorting detection, an intelligent detection method for coal and gangue, Our-v8, was proposed based on improved YOLOv8. Images of coal and gangue with different densities under two diverse lighting environments were collected. Then the Laplacian image enhancement algorithm was proposed to improve the training data quality, sharpening contours and boosting feature extraction; the CBAM attention mechanism was introduced to prioritize crucial features, enhancing more accurate feature extraction ability; and the EIOU loss function was added to refine box regression, further improving detection accuracy. The experimental results showed that Our-v8 for detecting coal and gangue in a halogen lamp lighting environment achieved excellent performance with a mean average precision (mAP) of 99.5%, was lightweight with FLOPs of 29.7, Param of 12.8, and a size of only 22.1 MB. Additionally, Our-v8 can provide accurate location information for coal and gangue, making it ideal for real-time coal sorting applications.

Improved YOLOv8 Model for Lightweight Pigeon Egg Detection

“…Research has shown that incorporating attention mechanisms and introducing upsamplers [33][34][35][36] have been proven to effectively enhance model detection accuracy. Zeng et al [37] proposed a YOLOv8 model based on the CBAM mechanism, which can effectively select key features of targets, achieving high-precision recognition of coal and gangue. Li et al [38] proposed an algorithm based on an improved YOLOv5s to achieve target detection and localization in tomato picking.…”