在岩石图像识别中，实现岩石快速准确的识别是岩石数字化发展的关键。其中，光照、湿度等环境因素引起的图像模糊问题成为岩石智能识别的最大挑战之一。基于此，提出了一种新的深度学习方法（MbileNetV3-small-RegNetX）来识别岩石图像，其适用于移动设备等资源有限的场景。在RegNet网络的基础上采用迁移学习方法，结合MobileNetV3残差结构与通道注意力（squeeze-and-excitation, SE）模块的优势，有效地优化了特征提取与网络结构，并显著提升了检测速度。为验证该方法的准确性，将新模型与当下主流的轻量化模型（DenseNet和ShuffleNet）进行消融对比实验。结果显示，所提模型表现出高精度（82.15%）、快速（0.06 GFLOPs）的特点。此外，该模型对于光照、湿度等环境因素引起的图像模糊具有良好的适应性。

In rock image recognition, achieving rapid and accurate identification of rocks is crucial for the digitalization of rocks. Among the challenges faced in intelligent rock recognition is the issue of image blurring caused by environmental factors such as lighting and humidity. In light of this, a novel deep learning approach (MobileNetV3-small-RegNetX) was proposed for rock image recognition, which is suitable for scenarios with limited resources such as mobile devices. Building upon the RegNet network, transfer learning methods, combining the advantages of the MobileNetV3 residual structure with squeeze-and-excitation (SE) modules was employed to effectively optimize feature extraction and network structure, leading to a significant improvement in detection speed. To validate the accuracy of this approach, comparative experiments were conducted between the new model and current mainstream lightweight models (DenseNet and ShuffleNet). The results demonstrate that the new model proposed exhibits high precision (82.15%) and fast processing (0.06 GFLOPs). Additionally, the model demonstrates good adaptability to environmental factors such as lighting and humidity-induced image blurring.