【摘要】 为提升复杂背景和岩石特征不清晰等条件下的识别效果,提出一种基于岩石视觉特征自适应融合的岩性智能识别方法,通过融合颜色、纹理和形状特征,实现对岩石图像的快速准确分类。首先,采用基于Panoptic FPN的方法对岩石图像进行自动标签生成,有效减少人工标注的工作量与时间成本;然后,引入基于Grad-CAM的岩石分割模型,通过热力图实现岩石与背景的像素级分割,从而降低背景信息对特征提取的干扰。在特征提取阶段,提取颜色、纹理和形状3类视觉特征,并采用积分梯度法评估其对分类决策的贡献;根据评估结果引入权重机制,对3类视觉特征进行自适应融合,并将融合结果与原图浅层特征联合表达,最终实现岩性的智能识别。试验结果表明:1)本方法在测试集上的分类准确率最高可达99.1%,较采用原始图像直接训练的模型提升3.5%,验证多特征融合与权重引导机制在提升岩性识别方面的有效性; 2)基于积分梯度的特征贡献度分析结果与人工判别具有较高的一致性,证明本方法在保证识别精度的基础上可增强模型的可解释性,具备在地质调查与岩土工程中应用的潜力。更多还原

【Abstract】 Lithology identification is a critical task in geology and geotechnical engineering. However, traditional methods heavily depend on professionals with geological expertise, making the process subjective, time-consuming, and laborintensive, which is unsuitable for large-scale or rapid deployment. To enhance identification performance in complex backgrounds and with ambiguous rock features, the authors propose an intelligent lithology identification method based on adaptive fusion of visual features. The method integrates color, texture, and shape features for fast and accurate classification of rock images. First, a Panoptic FPN approach based on the feature pyramid network is employed to automatically generate pixel-level annotations, considerably minimizing the need for manual labeling and reducing time costs. Next, a Grad-CAM-based segmentation model is utilized to isolate rocks from the background using class activation heatmaps, which reduces background interference and improves feature extraction accuracy. During feature extraction, color, texture, and shape features are extracted, and their contributions to classification are quantified using the integrated gradients method. Based on these evaluations, a weighting mechanism is introduced to adaptively fuse the three feature types. The resulting fused representation is then combined with shallow features from the original image to facilitate intelligent lithology identification. Experimental results show that:(1) the proposed method achieves a classification accuracy of up to 99.1% on the test set, representing a 3.5% improvement over models trained on original images. This confirms the effectiveness of the multifeature fusion and weight-guided mechanism in enhancing identification performance.(2) The feature contribution analysis based on integrated gradients shows strong alignment with manual interpretations, suggesting that the method not only improves model accuracy but also enhances interpretability, making it a valuable tool for geological surveys and geotechnical engineering.更多还原