With the extension of the service life of polyethylene gas pipelines, defect detection has become the core issue for ensure safety. To solve the problem of missed detection and insufficient accuracy in identifying internal defects of PE gas pipelines, this paper proposes an improved YOLOv8 target detection model. A new C2f-KS module is designed that has been optimized by introducing Kolmogorov-Arnold Networks into the innovative structure of bottleneck. In addition, the attention mechanism EffectiveSE is integrated after the split operation to distinguish effective information in complex backgrounds and enhance target features extraction. The three detection heads of YOLOv8 are modified to four, and EefConv convolution is introduced to reduce model complexity and parameter count, thus enhancing the sensitivity to small targets and effectively reducing the missed detection and false detection rates for small target foreign bodies. Finally, to optimize the precise positioning of the bounding box, the loss function Inner-Shape IOU is used. The experimental results show that the accuracy of the improved algorithm on the pipeline defect data set is 94.0%, the recall rate is 90.7%, the average accuracy is 94.2%, and the model size is only 4.9 MB, which can fully meet the needs of real-time detection of inner surface defects of PE gas pipelines.