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To improve the detection accuracy of (SSD) model for vibration damper, an attention mechanism based detection method is proposed. The method adopts ResNet as the backbone network instead of the VGG network, and introduces attention mechanism to improve the accuracy and speed of detection of vibration damper in transmission lines by extracting intermediate features through compression and using the weight coefficient to better distinguish the foreground and background. The introduced fused convolutional attention mechanism combines channel and spatial attention, and the performance jump is relatively obvious, while the computational efficiency is improved. A migration learning strategy is introduced to overcome the problem of difficult model training. The experimental results show that the SSD detection network model using the ResNet residual structure as the backbone and the fused convolutional attention mechanism improves the accuracy of seismic hammer detection in transmission lines by 2.5 percentage points,and completes vibration damper detection at 12fps. The recognition effect is significantly improved, which proves the effectiveness of the new algorithm.