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This paper presents a method for the automatic detection of sorbite content in high-carbon steel wire rods. A semantic segmentation model of sorbite based on DeepLabv3+ is established. The sorbite structure is segmented, and the prediction results are analyzed and counted based on the metallographic images of high-carbon steel wire rods marked manually. For the problem of sample imbalance, the loss function of Dice loss + focal loss is used, and the perturbation processing of training data is added. The results show that this method can realize the automatic statistics of sorbite content. The average pixel prediction accuracy is as high as 94.28%, and the average absolute error is only 4.17%. The composite application of the loss function and the enhancement of the data perturbation significantly improve the prediction accuracy and robust performance of the model. In this method, the detection of sorbite content in a single image only takes 10 s, which is 99% faster than that of 10 min using the manual cut-off method. On the premise of ensuring detection accuracy, the detection efficiency is significantly improved and the labor intensity is reduced. [ABSTRACT FROM AUTHOR]

Grain size has a significant effect on the mechanical properties of metals. It is very important to accurately rate the grain size number of steels. This paper presents a model for automatic detection and quantitative analysis of the grain size of ferrite–pearlite two-phase microstructure to segment ferrite grain boundaries. In view of the challenging problem of hidden grain boundaries in pearlite microstructure, the number of hidden grain boundaries is inferred by detecting them with the confidence of average grain size. The grain size number is then rated using the three-circle intercept procedure. The results show that grain boundaries can be accurately segmented by using this procedure. According to the rating results of grain size number of four types of ferrite–pearlite two-phase microstructure samples, the accuracy of this procedure is greater than 90%. The grain size rating results deviate from those calculated by experts using the manual intercept procedure by less than Grade 0.5—the allowable detection error specified in the standard. In addition, the detection time is shortened from 30 min of the manual intercept procedure to 2 s. The procedure presented in this paper allows automatic rating of grain size number of ferrite–pearlite microstructure, thereby effectively improving the detection efficiency and reducing the labor intensity. [ABSTRACT FROM AUTHOR]

Non-metallic inclusions are unavoidable defects in steel, and their type, quantity, size, and distribution have a great impact on the quality of steel. At present, non-metallic inclusions are mainly detected manually, which features high work intensity, low efficiency, proneness to misjudgment, and low consistency of results. In this paper, based on deep neural network algorithm, a small number of manually labeled, low-resolution metallographic images collected by optical microscopes are used as the dataset for intelligent boundary extraction, classification, and rating of non-metallic inclusions. The training datasets are cropped into those containing only a single non-metallic inclusion to reduce the interference of background information and improve the accuracy. To deal with the unbalanced distribution of each category of inclusions, the reweighting cross entropy loss and focal loss are respectively used as the category prediction loss and boundary prediction loss of the DeepLabv3+ semantic segmentation model. Finally, the length and width of the minimum enclosing rectangle of the segmented inclusions are measured to calculate the grade of inclusions. The resulting accuracy is 90.34% in segmentation and 90.35% in classification. As is verified, the model-based rating results are consistent with those of manual labeling. For a single sample, the detection time is reduced from 30 min to 15 s, significantly improving the detection efficiency. [ABSTRACT FROM AUTHOR]

Wheat stripe rust-damaged leaves present challenges to automatic disease index calculation, including high similarity between spores and spots, and difficulty in distinguishing edge contours. In actual field applications, investigators rely on the naked eye to judge the disease extent, which is subjective, of low accuracy, and essentially qualitative. To address the above issues, this study undertook a task of semantic segmentation of wheat stripe rust damage images using deep learning. To address the problem of small available datasets, the first large-scale open dataset of wheat stripe rust images from Qinghai province was constructed through field and greenhouse image acquisition, screening, filtering, and manual annotation. There were 33,238 images in our dataset with a size of 512 × 512 pixels. A new segmentation paradigm was defined. Dividing indistinguishable spores and spots into different classes, the task of accurate segmentation of the background, leaf (containing spots), and spores was investigated. To assign different weights to high- and low-frequency features, we used the Octave-UNet model that replaces the original convolutional operation with the octave convolution in the U-Net model. The Octave-UNet model obtained the best benchmark results among four models (PSPNet, DeepLabv3, U-Net, Octave-UNet), the mean intersection over a union of the Octave-UNet model was 83.44%, the mean pixel accuracy was 94.58%, and the accuracy was 96.06%, respectively. The results showed that the state-of-art Octave-UNet model can better represent and discern the semantic information over a small region and improve the segmentation accuracy of spores, leaves, and backgrounds in our constructed dataset. [ABSTRACT FROM AUTHOR]