Vibration dampers are critical hardware components and high-priority inspection targets in UAV-based power line surveys.Constrained by safety distances and camera perspectives, vibration dampers in inspection images often exhibit small pixel occupancy and subtle defect features, posing significant challenges for fine-grained defect detection.To address this, we propose a cascaded defect detection method for vibration dampers based on linear global attention and expert geometric knowledge, decoupling the fine-grained detection task into localization and classification stages.In the localization stage, a lightweight linear global attention-based network is developed to enhance global contextual awareness while maintaining real-time performance.In the defect classification stage, an expert geometric knowledge embedded network is presented to formalize geometric priors into explicit feature representations, augmenting the semantic comprehension of defect categories.Experimental results on a custom dataset show that the localization network achieves 84.1% precision at a 95% recall rate, and the classification accuracy reaches 93.42%, validating the efficacy of the proposed method for fine-grained defect detection.