Abstract
The surface defects’ region of strip steel is small, and has various defect types and, complex gray structures. There tend to be a large number of false defects and edge light interference, which lead traditional machine vision algorithms to be unable to detect defects for various types of strip steel. Image detection techniques based on deep learning require a large number of images to train a network. However, for a dataset with few samples with category imbalanced defects, common deep learning neural network training tasks cannot be carried out. Based on rapid image preprocessing algorithms (improved gray projection algorithm, ROI image augmentation algorithm) and transfer learning theory, this paper proposes a set of processes for complete strip steel defect detection. These methods achieved surface rapid screening, defect feature extraction, sample dataset’s category balance, data augmentation, defect detection, and classification. Through verification of the mixed dataset, composed of the NEU surface dataset and dataset in this paper, the recognition accuracy of the improved VGG19 network in this paper reached 97.8%. The improved VGG19 network performs slightly better than the baseline VGG19 in six types of defects, but the improved VGG19 performs significantly better in the surface seams defects. The convergence speed and accuracy of the improved VGG19 network were taken into account, and the detection rate was greatly improved with few samples and imbalanced datasets. This paper also has practical value in terms of extending its method of strip steel defect detection to other products.
Highlights
The main contributions of the paper are summarized below: (1) For strip steel with a low defect rate, edge false defects and illumination interference, which make it difficult to extract various kinds of real defects, we present an improved strip steel surface defect detection algorithm based on gray-scale projection
According to Author [6], surface defect detection algorithms can be divided into four categories: traditional statistical-based algorithms, spectrum-based algorithms, modelbased algorithms, and emerging deep learning algorithms
For deep learning detection algorithms, their detection accuracy increases with data volume and their real-time performance is better than that of the traditional algorithms
Summary
Strip steel is one of the main products of the iron and steel industry. It is an indispensable raw material of shipbuilding, automobiles, machinery manufacturing and other industries. In traditional iron and steel enterprises, the surface quality of high-speed moving strip steel is usually detected by an artificial naked eye stroboscopic method. Due to strip steel’s rapid movement on the production line, it will produce a large amount of image data (for example, 25 frames/sec), which demands an excellent real-time defect detection algorithm. It can lead images captured by CCD cameras to be of low resolution and less qualified. Algorithms prefer that the categories contain plentiful samples and that each category contains a similar number of samples
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