A novel multi-model cascade framework for pipeline defects detection based on machine vision

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.