Song Jian, Tian Yingzhong, Wan Xiang
Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China.
Institute of Applied Physics, Jiangxi Academy of Sciences, Nanchang 330000, China.
Sensors (Basel). 2024 Dec 11;24(24):7914. doi: 10.3390/s24247914.
Although approaches for the online surface detection of automotive pipelines exist, low defect area rates, small-sample and long-tailed data, and the difficulty of detection due to the variable morphology of defects are three major problems faced when using such methods. In order to solve these problems, this study combines traditional visual detection methods and deep neural network technology to propose a transfer learning multi-channel fusion decision network without significantly increasing the number of network layers or the structural complexity. Each channel of the network is designed according to the characteristics of different types of defects. Dynamic weights are assigned to achieve decision-level fusion through the use of a matrix of indicators to evaluate the performance of each channel's recognition ability. In order to improve the detection efficiency and reduce the amount of data transmission and processing, an improved ROI detection algorithm for surface defects is proposed. It can enable the rapid screening of target surfaces for the high-quality and rapid acquisition of surface defect images. On an automotive pipeline surface defect dataset, the detection accuracy of the multi-channel fusion decision network with transfer learning was 97.78% and its detection speed was 153.8 FPS. The experimental results indicate that the multi-channel fusion decision network could simultaneously take into account the needs for real-time detection and accuracy, synthesize the advantages of different network structures, and avoid the limitations of single-channel networks.
虽然存在用于汽车管道在线表面检测的方法，但低缺陷面积率、小样本和长尾数据以及由于缺陷形态变化导致的检测困难是使用此类方法时面临的三个主要问题。为了解决这些问题，本研究将传统视觉检测方法与深度神经网络技术相结合，在不显著增加网络层数或结构复杂度的情况下，提出了一种迁移学习多通道融合决策网络。网络的每个通道根据不同类型缺陷的特征进行设计。通过使用指标矩阵来评估每个通道的识别能力表现，分配动态权重以实现决策级融合。为了提高检测效率并减少数据传输和处理量，提出了一种改进后的表面缺陷ROI检测算法。它能够快速筛选目标表面，以高质量、快速地获取表面缺陷图像。在一个汽车管道表面缺陷数据集上，具有迁移学习的多通道融合决策网络的检测准确率为97.78%，检测速度为153.8帧每秒。实验结果表明，多通道融合决策网络能够同时兼顾实时检测和准确性的需求，综合不同网络结构的优势，避免单通道网络的局限性。
