Improved ResNet-Based Multi-Dimensional Evaluation Framework for Power Industry Image Analysis

With the growing demand for image quality assessment in the power industry, traditional single-dimensional evaluation methods can no longer meet the multi-dimensional practical requirements. This study proposes an improved ResNet-based multi-task learning framework for comprehensive power image quality assessment. By introducing a multi-task learning mechanism, the proposed method can simultaneously evaluate multiple dimensions, including image clarity, lighting adaptability, equipment recognition consistency, and noise levels. Through the optimization of a feature fusion module and attention mechanism, the method significantly enhances the synergy among different tasks. Experimental results demonstrate that the proposed model performs excellently across multiple evaluation dimensions, providing more accurate support for intelligent power equipment detection and improving the practical application of power images.