Abstract
Abstract As core technical documents for the process industry, Piping and Instrumentation Diagrams (P\&IDs) and Process Flow Diagrams (PFDs) face severe challenges in automated object detection: small industrial symbols (e.g., arrows, stream numbers, valves) occupy minimal image areas, leading to insufficient feature extraction; limited labeled samples result in poor model generalization under few-shot conditions; and dense symbol distribution with complex background interference causes high false detection and missed detection rates. To tackle these issues, this study constructs a dedicated multi-industry dataset consisting of 182 annotated P\&IDs/PFDs, covering 308 core object categories and 118,723 annotation samples from petrochemical and coal chemical industries. Furthermore, we propose a Hybrid Strategy Object Detection (HSOD) workflow that integrates parallel adaptive multi-scale detection and hybrid post-processing. Based on YOLOv12, the workflow employs three-level adaptive patch Slicing Aided Hyper Inference (SAHI) to enhance small object feature extraction, and designs a hybrid filtering strategy combining position-based confidence calibration, adaptive Non-Maximum Suppression (NMS), and quartile-based confidence thresholding to eliminate duplicate detections and false positives. Extensive experiments show that our HSOD workflow outperforms mainstream baselines (RT-DETR, YOLOv12, Faster R-CNN) on all metrics. Compared with the RT-DETR baseline, our method improves mAP@0.5 by 23.27%, mAP@0.5:0.95 by 33.09%, and the recall of small objects by 85.86%. Qualitative comparison with Gemini 3.1 Pro further validates the superiority of specialized detection models. This work provides a reliable solution for the intelligent digital parsing of industrial drawings, supporting the intelligent operation, maintenance and digital twin construction of process industries.