PipeMFL-240K: A Large-scale Dataset and Benchmark for Object Detection in Pipeline Magnetic Flux Leakage Imaging

2026-02-04

AI Summary

This work addresses the long-standing limitation in automatic defect detection for magnetic flux leakage (MFL) imaging—namely, the absence of large-scale public datasets and standardized benchmarks. To bridge this gap, we introduce PipeMFL-240K, the first large-scale object detection dataset and benchmark specifically designed for pipeline MFL pseudocolor images. It comprises over 240,000 images and 190,000 high-quality bounding box annotations across 12 defect categories, faithfully capturing real-world challenges such as long-tailed class distribution, small object scales, and high intra-class variability. Leveraging this dataset, we conduct a systematic evaluation of state-of-the-art detection models, revealing their performance bottlenecks in MFL scenarios and establishing a reliable, reproducible platform to foster future algorithmic innovation and pipeline integrity assessment.

Technology Category

• Computer Vision: Object Detection & Categorization
• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Pipeline integrity is critical to industrial safety and environmental protection, with Magnetic Flux Leakage (MFL) detection being a primary non-destructive testing technology. Despite the promise of deep learning for automating MFL interpretation, progress toward reliable models has been constrained by the absence of a large-scale public dataset and benchmark, making fair comparison and reproducible evaluation difficult. We introduce \textbf{PipeMFL-240K}, a large-scale, meticulously annotated dataset and benchmark for complex object detection in pipeline MFL pseudo-color images. PipeMFL-240K reflects real-world inspection complexity and poses several unique challenges: (i) an extremely long-tailed distribution over \textbf{12} categories, (ii) a high prevalence of tiny objects that often comprise only a handful of pixels, and (iii) substantial intra-class variability. The dataset contains \textbf{240,320} images and \textbf{191,530} high-quality bounding-box annotations, collected from 11 pipelines spanning approximately \textbf{1,480} km. Extensive experiments are conducted with state-of-the-art object detectors to establish baselines. Results show that modern detectors still struggle with the intrinsic properties of MFL data, highlighting considerable headroom for improvement, while PipeMFL-240K provides a reliable and challenging testbed to drive future research. As the first public dataset and the first benchmark of this scale and scope for pipeline MFL inspection, it provides a critical foundation for efficient pipeline diagnostics as well as maintenance planning and is expected to accelerate algorithmic innovation and reproducible research in MFL-based pipeline integrity assessment.

Problem

• Magnetic Flux Leakage
• Object Detection
• Pipeline Inspection
• Dataset
• Benchmark

Innovation

• Magnetic Flux Leakage (MFL)
• large-scale dataset
• object detection
• pipeline inspection
• long-tailed distribution