Mineral identification in thin sections using a lightweight and attention mechanism

Mineral identification is foundational to geological survey research, mineral resource exploration, and mining engineering. Considering the diversity of mineral types and the challenge of achieving high recognition accuracy for similar features, this study introduces a mineral detection method based on YOLOv8-SBI. This work enhances feature extraction by integrating spatial pyramid pooling-fast (SPPF) with the simplified self-attention module (SimAM), significantly improving the precision of mineral feature detection. In the feature fusion network, a weighted bidirectional feature pyramid network is employed for advanced cross-channel feature integration, effectively reducing feature redundancy. Additionally, Inner-Intersection Over Union (InnerIOU) is used as the loss function to improve the average quality localization performance of anchor boxes. Experimental results show that the YOLOv8-SBI model achieves an accuracy of 67.9 %, a recall of 74.3 %, a [email protected] of 75.8 %, and a [email protected]:0.95 of 56.7 %, with a real-time detection speed of 244.2 frames per second. Compared to YOLOv8, YOLOv8-SBI demonstrates a significant improvement with 15.4 % increase in accuracy, 28.5 % increase in recall, and increases of 28.1 % and 20.9 % in [email protected] and [email protected]:0.95, respectively. Furthermore, relative to other models, such as YOLOv3, YOLOv5, YOLOv6, YOLOv8, YOLOv9, and YOLOv10, YOLOv8-SBI has a smaller parameter size of only 3.01 × 10⁶. This highlights the optimal balance between detection accuracy and speed, thereby offering robust technical support for intelligent mineral classification.