用深度学习自动分析钙钛矿太阳能电池的扫描电镜图像，精准识别材料相与缺陷。
Automated and Scalable SEM Image Analysis of Perovskite Solar Cell Materials via a Deep Segmentation Framework
- 基于改进YOLOv8x架构，融合自适应卷积与可分离下采样模块
- 分割准确率达87.25%，比基线提升4.08%，模型更小更快
- 可量化晶粒面积与数量，适合材料研发与工艺优化人员使用
扫描电子显微镜（SEM）在钙钛矿太阳能电池薄膜制备过程中对微观结构表征至关重要。准确识别和量化未反应的碘化铅与钙钛矿相极为关键，因为残留碘化铅影响结晶路径与缺陷生成，而钙钛矿晶粒形貌决定载流子传输与器件稳定性。然而当前SEM图像分析仍以人工为主，限制了效率与一致性。本文提出一种基于深度学习的自动化框架PerovSegNet，实现对多种形貌下碘化铅、钙钛矿及缺陷区域的精准分割。该模型基于改进的YOLOv8x架构，引入两个新模块：(i) 自适应洗牌空洞卷积块，通过分组卷积与通道混合增强多尺度细粒度特征提取；(ii) 可分离自适应下采样模块，协同保留细尺度纹理与大尺度结构，提升边界识别鲁棒性。在包含10,994张SEM图像的增强数据集上训练，PerovSegNet达到87.25%的平均精度，仅需265.4 Giga浮点运算，较基线YOLOv8x-seg提升4.08%，同时模型规模与计算负载分别减少24.43%与25.22%。除分割外，框架还提供晶粒级定量指标，如碘化铅/钙钛矿面积与数量，可作为结晶效率与微结构质量的可靠指标。该工具适用于实时工艺监控与数据驱动的钙钛矿薄膜制备优化。源码已公开：https://github.com/wlyyj/PerovSegNet/tree/master。
原文摘要 · Abstract (English)
Scanning Electron Microscopy (SEM) is indispensable for characterizing the microstructure of thin films during perovskite solar cell fabrication. Accurate identification and quantification of lead iodide and perovskite phases are critical because residual lead iodide strongly influences crystallization pathways and defect formation, while the morphology of perovskite grains governs carrier transport and device stability. Yet current SEM image analysis is still largely manual, limiting throughput and consistency. Here, we present an automated deep learning-based framework for SEM image segmentation that enables precise and efficient identification of lead iodide, perovskite and defect domains across diverse morphologies. Built upon an improved YOLOv8x architecture, our model named PerovSegNet incorporates two novel modules: (i) Adaptive Shuffle Dilated Convolution Block, which enhances multi-scale and fine-grained feature extraction through group convolutions and channel mixing; and (ii) Separable Adaptive Downsampling module, which jointly preserves fine-scale textures and large-scale structures for more robust boundary recognition. Trained on an augmented dataset of 10,994 SEM images, PerovSegNet achieves a mean Average Precision of 87.25% with 265.4 Giga Floating Point Operations, outperforming the baseline YOLOv8x-seg by 4.08%, while reducing model size and computational load by 24.43% and 25.22%, respectively. Beyond segmentation, the framework provides quantitative grain-level metrics, such as lead iodide/perovskite area and count, which can serve as reliable indicators of crystallization efficiency and microstructural quality. These capabilities establish PerovSegNet as a scalable tool for real-time process monitoring and data-driven optimization of perovskite thin-film fabrication.The source code is available at:https://github.com/wlyyj/PerovSegNet/tree/master.
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