At present, quality control is becoming a major issue in steel production. Thus we developed an algorithm that uses machine vision to detect scarfing faults on slabs, which impairs the steel quality of subsequent products such as steel plates. Scarfing faults typically occur in three locations: the top, middle, and edge of the slab. Our proposed algorithm is focused on detecting scarfing faults on the edge of slab, which is tiny and sometimes indistinct. A machine vision system with a line scan camera was designed, which facilitates the detection of brightness differences and texture differences between well-scarfed and poorly-scarfed slab surface. Scarfing faults are tiny on the edges, so we propose a new segmentation method that takes advantage of capabilities of the line scan camera. A segmented image is filtered using Gabor filters, which were designed to focus on the boundary with scarfing faults to identify specific regions with defect, referred to as defect candidates. Each defect candidate is classified using a Support Vector Machine (SVM) classifier based on its extracted features. Our proposed algorithm was effective according to the experimental trials using 2 061 frame images acquired from real samples, where the true detection rate was 97.26% and the false detection rate was 1.66%. Our proposed system and algorithm based on machine vision technology facilitates scarfing faults detection, which can be detected before rolling process, resulting in improved steel quality.

We proposed a scheme for adaptively selecting filter parameters for detecting defects in various image textures. To implement the proposed scheme on a target steel image, we used wavelet reconstruction method. The adaptive parameter-selecting scheme was presented by analyzing the textures in an image and obtaining the appropriate parameters from a pretrained neural network by inputting these texture features. Experiments were conducted to detect corner cracks in the images of a steel billet, and the proposed scheme was compared with a conventional wavelet reconstruction method. The experimental results showed that our proposed scheme was effective in detecting defects in various textures of the target images.

Sensor data from missile flights are highly valuable, as a test requires considerable resources, but some sensors may be detached or fail to collect data. Remotely acquired missile sensor data are incomplete, and the correlations between the missile data are complex, which results in the prediction of sensor data being difficult. This article proposes a deep learning-based prediction network combined with the wavelet analysis method. The proposed network includes an imputer network and a prediction network. In the imputer network, the data are decomposed using wavelet transform, and the generative adversarial networks assist the decomposed data in reproducing the detailed information. The prediction network consists of long short-term memory with an attention and dilation network for accurate prediction. In the test, the actual sensor data from missile flights were used. For the performance evaluation, the test was conducted from the data with no missing values to the data with five different missing rates. The test results showed that the proposed system predicts the missile sensor most accurately in all cases. In the frequency analysis, the proposed system has similar frequency responses to the actual sensors and showed that the proposed system accurately predicted the sensors in both tendency and frequency aspects.