To address challenges in detecting missing Shockproof Hammers on transmission lines due to their small size, complex image backgrounds, and subtle presence, this study proposes a lightweight YOLOv8-SPH model for damper absence detection. The model introduces shallow-scale feature maps of 160×160 and 320×320 within the neck of the YOLOv8n network and integrates multi-scale detection modules within the detection head. This enhances contextual information fusion across feature maps, effectively expanding the receptive field, enabling the model to capture richer semantic features related to damper absence. An innovative multi-scale high-efficiency feature extraction module (MultFaster) is also introduced, utilizing partial convolutions, multi-level feature extraction, and residual connections. This structure maintains detection accuracy for damper features while reducing computational complexity and parameter load. Additionally, a dynamic upsampling operator is incorporated into the neck network to improve feature map resolution, improving the model’s accuracy in detecting missing Shockproof Hammers. To further optimize, the original model’s decoupled detection head is replaced with a lightweight detection head, reducing computational complexity and boosting detection efficiency. The enhanced network undergoes amplitude-based layer-adaptive sparse pruning, significantly reducing model parameters and computational load. Testing on a custom damper absence dataset demonstrates YOLOv8-SPH exhibited remarkable performance, achieving an mAP@0.5 of 91.51%, which marks a 6.3% improvement over the original YOLOv8n. Additionally, parameter count is reduced by 80.73%, computational load by 48.14%, and model size by 62.41%. The model achieves improved detection accuracy while reducing computational complexity and parameter size, effectively meeting the demands for efficient and precise detection of Shockproof Hammers in transmission lines, showcasing significant practical value.