With the development of industrialization, the demand for high-performance metal materials has increased, and copper and its alloys have been widely used. The microstructure of these materials significantly affects their performance. To address the issues of subjectivity, low efficiency, and limited quantitative capability in traditional metallographic analysis methods, this paper proposes a deep learning-based approach for segmenting the second phase in Cu-Fe alloys. The method is built upon the YOLO11 framework and incorporates a series of structural enhancements tailored to the characteristics of the secondary-phase microstructure, aiming to improve the model’s detection accuracy and segmentation performance. Specifically, the EIEM module enhances the C3K2 structure to improve edge perception; the CSPSA module is optimized into C2CGA to strengthen multi-scale feature representation; and the RepGFPN and DySample techniques are integrated to construct the GDFPN neck network. Experimental results on the Cu-Fe alloy metallographic image dataset demonstrate that YOLO11 outperforms mainstream semantic segmentation models such as U-Net and DeepLabV3+ in terms of mAP (85.5%), inference speed (208 FPS), and model complexity (10.2 GFLOPs). The improved YOLO11 model achieves an mAP of 89.0%, a precision of 84.6%, and a recall of 81.0% on this dataset, showing significant performance improvements while effectively balancing inference speed and model complexity. Additionally, a quantitative analysis software system for secondary phase uniformity based on this model provides strong technical support for automated metallographic image analysis and demonstrates broad application prospects in materials science research and industrial quality control.

Copper electrolysis is a crucial process in copper smelting. The surface of cathodic copper plates is often affected by various electrolytic process factors, resulting in the formation of nodule defects that significantly impact surface quality and disrupt the downstream production process, making the prompt detection of these defects essential. At present, the detection of cathode copper plate nodules is performed by manual identification. In order to address the issues with manual convex nodule identification on the surface of industrial cathode copper plates in terms of low accuracy, high effort, and low efficiency in the manufacturing process, a lightweight YOLOv5 model combined with the BiFormer attention mechanism is proposed in this paper. The model employs MobileNetV3, a lightweight feature extraction network, as its backbone, reducing the parameter count and computational complexity. Additionally, an attention mechanism is introduced to capture multi-scale information, thereby enhancing the accuracy of nodule recognition. Meanwhile, the F-EIOU loss function is employed to strengthen the model’s robustness and generalization ability, effectively addressing noise and imbalance issues in the data. Experimental results demonstrate that the improved YOLOv5 model achieves a precision of 92.71%, a recall of 91.24%, and a mean average precision (mAP) of 92.69%. Moreover, a single-frame detection time of 4.61 ms is achieved by the model, which has a size of 2.91 MB. These metrics meet the requirements of practical production and provide valuable insights for the detection of cathodic copper plate surface quality issues in the copper electrolysis production process.