Detecting composite insulator hydrophobicity class is critical in power system inspections. This study proposes an intelligent recognition method for the hydrophobicity class of composite insulators based on MSG-SSD to address the challenges of low detection efficiency, poor real-time performance, and complex model structures in existing methods. Firstly, the detection model is based on the SSD algorithm, employing the lightweight MobileNetV2 as the backbone network to simplify the network and significantly enhance detection speed. Secondly, to improve the extraction capability of watermark features, a high-resolution feature fusion module, Sim-HRFPN, is constructed, which retains high-resolution features during the fusion process to compensate for the accuracy loss caused by the lightweight design. Finally, to further enhance the computational efficiency of the model, traditional convolution is replaced with GhostConv in the additional prediction feature layers, thereby significantly reducing the computational burden while maintaining the high performance of the model. The results indicate that, compared to SSD, MSG-SSD achieves a 48.17% improvement in detection speed and a 4.89% improvement in accuracy, while reducing computational cost and parameter count by 97.63% and 82.99%, respectively. From this, it can be concluded that the improved model accurately identifies and rapidly locates the hydrophobicity class of composite insulators and meets the lightweight deployment requirements of edge inspection devices. This provides an effective method for the intelligent detection of the operational status of composite insulators in power systems.