To address the challenges associated with complex background textures on the inner walls of water pipelines and the difficulty in distinguishing minute cracks from irregular fouling, this paper proposes a high-precision detection algorithm based on an improved YOLOv11n. First, the bidirectional feature pyramid network (BiFPN) is utilized to reconstruct the neck network. By employing a fast normalized fusion mechanism, this approach achieves dynamic aggregation of multi-scale features, thereby enhancing the feature representation capability for minute and variable-scale targets. Second, a global context network (GCNet) module is embedded at the end of the feature fusion stage to establish pixel-level long-range dependencies. This strengthens global background understanding and effectively suppresses noise interference such as limescale. Finally, a Focaler-MPDIoU loss function is constructed by integrating geometric optimization with a dynamic focusing mechanism to improve localization accuracy and balance the training of hard and easy samples. Experimental results demonstrate that the improved algorithm achieves an mAP50 of 91.40% on a self-constructed dataset, representing an improvement of approximately 9.5% over the baseline. The proposed method significantly enhances robustness while satisfying real-time requirements, offering substantial value for engineering applications.