A lightweight YOLOv8-based model with Squeeze-and-Excitation Version 2 for crack detection of pipelines

Crack detection is crucial to the buried pipelines that transit water, gas, oil, etc. However, the traditional detection methods may lack accuracy and robustness for the pipelines in low-light and complex backgrounds. This study proposes a YOLOv8-GhostConv-SEV2 model based on the lightweight YOLOv8n framework, which optimizes feature extraction by introducing the GhostConv module and enhances noise suppression capability with the SEV2 (Squeeze-and-Excitation Version 2) attention mechanism. Based on a dataset of 11,135 images of pipelines in a low-light environment, the proposed model achieves 98.1 % (+2.62 %) precision, 95.7 % (+3.80 %) recall, 0.969 (+3.30 %) F1 score, and 82.4 % (+11.49 %) mAP50–95 on the test set. Additionally, the improved model size is only 5.67 MB (-5.34 %), which is lightweight and highly suitable for the crack detection of buried pipelines in complex environments.