摘要：Defect detection technology is vital for ensuring the safety of pipelines during transportation. However, the current methods for defect detection using machine vision rely on having enough labeled defect samples. Unfortunately, some specific defect samples are difficult to obtain in engineering practice, which creates an imbalanced data problem and limits detection performance. Furthermore, traditional methods struggle to achieve satisfactory results with low-quality images. To solve these problems, a novel multi-model cascade framework based on machine vision is proposed. This framework uses a modified Super-Resolution Generative Adversarial Network (MSRGAN) with a self-attention mechanism to generate high-quality fake defect samples to balance data distribution. An improved Visual Geometry Group network (IVGG16) is also designed to enhance the performance of imbalanced defect classification, and Mask R-CNN is utilized to locate the defects. The experimental results demonstrate that the proposed framework performs well in recognizing imbalanced and low-quality samples, and it outperforms other state-of-the-art methods in terms of detection accuracy. ? 2023 Elsevier Ltd

ISSN号：0263-2241

卷、期、页：卷220

发表日期：2023-10-01

影响因子：0.000000

期刊分区（SCI为中科院分区）：二区

收录情况：EI（工程索引）

发表期刊名称：Measurement: Journal of the International Measurement Confederation

通讯作者：高博轩,苗兴园

第一作者：赵弘

论文类型：期刊论文

论文概要：高博轩,赵弘,苗兴园，A novel multi-model cascade framework for pipeline defects detection based on machine vision，Measurement: Journal of the International Measurement Confederation，2023，卷220

论文题目：A novel multi-model cascade framework for pipeline defects detection based on machine vision