Recognition of dispersed organic matter macerals using YOLOv5m model with convolutional block attention module

Abstract

The identification of dispersed organic matter (DOM) macerals plays a crucial role in petroleum geology. However, the conventional manual statistical methods are both time-consuming and laborious. With advancements in deep learning, automatic computerized identification of macerals has become the prevailing trend. In this study, we present the first application of the YOLOv5m lightweight semantic segmentation model with convolutional block attention module (CBAM) for classifying DOM macerals into five distinct categories, namely vitrinite, inertinite, solid bitumen, sporinite, and alginite. The results demonstrate that the YOLOv5m + CBAM model exhibits a superior convergence rate, as evidenced by the loss and precision-recall curves. It also achieves a 2 % increase in mean average precision at 0.5 intersection over union threshold over the YOLOv5m model. Moreover, the YOLOv5m + CBAM model shows robustness and efficiency in DOM macerals segmentation, achieving a mean intersection over union of 53.4 % and a mean pixel accuracy of 72.2 % (excluding the background category), while maintaining similar parameters and floating-point operations comparable to those of YOLOv5m. Comparative analysis against models such as DeepLab v3 + and U-net reveals that YOLOv5m + CBAM achieves superior accuracy with fewer parameters, particularly on smaller datasets. Statistical analysis confirms its consistency with manual labeling, resulting in a mean absolute percentage error of 7.34 %. Consequently, the proposed YOLOv5m + CBAM framework represents an ideal approach for pixel segmentation of DOM macerals.