Accurate identification of slope soil types is vital for stability assessment and protective design in transmission engineering. However, conventional field surveys and laboratory analyses have low efficiency and high subjectivity, thus making them difficult to satisfy the requirements for real-time identification in complex engineering scenarios. To this end, this study proposes a lightweight deep learning model based on texture integration SSR-MobileNetV2-T, which employs a dual-branch network structure combining multi-scale Gabor filtering and local binary pattern (LBP) to enhance the ability to extract soil micro-texture. Meanwhile, a multi-source soil image dataset is constructed, and the samples are enlarged via HSV-based threshold segmentation and diverse data augmentation to simulate complex field conditions and thus train the model in an end-to-end manner. The experiments show that on a five-class slope-soil image classification task, the SSR-MobileNetV2-T model achieves an average accuracy of 98.1% and an F1-score of 97.9%, generally outperforming typical lightweight models such as SVM, CNN, and EfficientNet, with prominent performance for gravel and sandy soils in particular. Parameter sensitivity analysis and ablation experiments confirm the effectiveness of each module’s design. The study indicates that SSR-MobileNetV2-T is both lightweight and highly accurate, providing efficient and reliable technical support for the intelligent identification of slope soil in transmission projects.