In industrial keypoint detection, image binarization is commonly used to extract industrial component contour regions, but this process often loses structure-related contextual information while resulting in extremely sparse foreground regions containing critical structures, severely degrading keypoint localization accuracy. To address these challenges, we propose a category-guided keypoint detection framework specifically designed for industrial binary images. The framework introduces a semantic classification embedding module integrated into a U-Net backbone, which explicitly embeds category information to effectively guide keypoint localization. Additionally, we design a balanced hybrid loss function based on regional stratification and normalized weighting, significantly enhancing the stability and precision of heatmap predictions under foreground-background imbalance. To validate our approach, we construct and publicly release an industrial dataset comprising three geometrically distinct insulator categories, providing annotations for both keypoint localization and classification. Experimental results demonstrate that our method achieves superior accuracy under sparse, imbalanced, and binarized conditions. Our framework provides a practical solution for precise and stable keypoint detection in structured industrial applications. Code and dataset are available at: https://github.com/TianqiNee/CategoryKeypointNet .