Improved U-Net for Precise Gauge Dial Segmentation in Substation Inspection Systems: A Study on Enhancing Accuracy and Robustness

In practical applications, the clarity of analog dial images is often compromised due to factors such as lighting conditions, leading to low precision and poor segmentation of dial scales and pointers. This results in segmentation outcomes that fail to meet the real-time requirements of substation inspection systems. To address these challenges, we propose an improved U-Net segmentation algorithm. The key innovation of our approach is the insertion of a layer-hopping connection module between the Encoder and Decoder to capture feature information across multiple scales, enhancing semantic expressiveness and optimizing feature fusion. Additionally, we replace traditional convolution operations with wavelet convolution, which improves the network’s ability to capture low-frequency information, essential for understanding the overall dial structure. An adaptive attention mechanism is also incorporated in the upsampling stage of the network, enabling the model to dynamically focus on salient features, further improving generalization. These improvements enable the network to more accurately detect target regions within dial images, significantly enhancing segmentation accuracy and robustness. Experimental results demonstrate that the proposed method outperforms traditional U-Net models in segmentation tasks, achieving superior precision in segmenting scales and pointers, effectively addressing issues of low precision and poor segmentation, and making it suitable for real-time substation inspection systems.