An optimized visual measurement method for cell parallelism based on edge-aware dynamic re-weighted U-Net (EADRU-Net)

Due to the reflective surfaces of battery cells, which introduce ambiguities during visual inspection, segmenting light strip edges accurately and extracting the center of the light strip become challenging. These tasks are crucial for measuring the parallelism between cells. To tackle this issue, this paper introduces a novel neural network model named Edge-Aware Dynamic Re-weighted U-Net (EADRU-Net). This model significantly improves edge detection and segmentation by incorporating an Edge Emphasis Loss. Moreover, we integrate a Context-Aware Cross-Dimensional Adaptive Attention mechanism. This mechanism optimizes the capture and expression of key features of light strips through context-aware layers and cross-dimensional learning strategies. EADRU-Net features a dynamic re-weighting mechanism that adaptively adjusts the weight of each pixel, optimizing the recognition and segmentation of reflective light strips on cell surfaces. Experimental results demonstrate EADRU-Net’s superior performance in noise suppression and precise edge segmentation of light strips, achieving a Mean Intersection over Union of 90.95% and a Mean Pixel Accuracy of 93.89%. This represents a 3.94% improvement over the enhanced U-Net, highlighting EADRU-Net’s effectiveness and superiority in detecting and segmenting light strips on cell surfaces.