FPE‑YOLO: Real‑Time Small‑Object Detection for Third‑Party Intrusion in Oil and Gas Pipeline Integrity Monitoring

• A bio-inspired FPE-YOLO framework for small object detection in pipeline intrusion monitoring. • Progressive three-stage enhancement: attention → cross-scale fusion → feature enhancement. • FoveaAttention improves localization via multi‑scale center‑region features. • E‑DCFP embeds foveal attention in bidirectional pyramids for adaptive scales. • 99.14% mAP@0.5, 97.26% mAP@0.5:0.95 at 65.4 FPS; +4.91/+4.59 vs YOLOv8n. As oil and gas pipeline networks expand, third-party intrusions such as mechanical excavation, illegal construction, and unauthorized presence pose serious threats to pipeline integrity, affecting safety and causing unexpected outages. We introduce FPE‑YOLO, an enhanced YOLOv8-based system designed for real-time detection of intrusions, especially under challenging conditions involving small, sparse objects and significant background interference along rights‑of‑way. Inspired by foveal vision, our approach employs a progressive method: bio-inspired attention, cross-level fusion, and single-level enhancement, comprising three modules: FoveaAttention, E-DCFP, and PSEM. Fovea-guided adaptive fusion and small-object enhancement work together to detect and represent subtle intrusions accurately. On the FPE dataset, which contains 6,750 images with 75.8% small objects, FPE‑YOLO achieves a mAP@0.5 of 99.14% and a mAP@0.5:0.95 of 97.26%, outperforming YOLOv8n by +4.91 and +4.59 percentage points, respectively. It operates in real time at 65.4 FPS, with only a 19.5% increase in computational complexity. These results demonstrate the framework’s effectiveness and suitability for continuous pipeline monitoring, enabling earlier detection and reducing operational risks in energy infrastructure.