Surface defect detection and classification in mandarin fruits using fuzzy image thresholding, binary wavelet transform and linear classifier model

A nondestructive testing method for detecting surface defects of Si3N4-Bearing cylindrical rollers based on an optimized convolutional neural network

Quality control in metal product manufacturing relies heavily on accurately detecting and classifying surface defects through visual inspection. Recently, convolutional neural networks (CNNs) have shown promising results in automating this process with high accuracy. This research paper proposes a new (experimental version) Lite Convolutional Neural Network (LCNN) designed to analyze image data to detect and classify surface defects on metallic surfaces. Our model was trained on a metal surface defects dataset comprising 1800 images of six different types of surface defects. Despite using relatively small datasets, the proposed LCNN version achieves a classification accuracy of 91.67%, highlighting its effectiveness in real-world defect detection scenarios.

Cylinder liner is an internal part of the automobile engine, which plays an important role in the automobile internal combustion engine. Therefore, it is a top priority to accurately and quickly detect the cylinder liner surface defects. In order to effectively achieve the classification and localization of surface defects on the cylinder liner, this paper establishes a dataset for surface defects on cylinder liner and proposes a based on improved YOLOv5 algorithm for detecting surface defects on cylinder liner. Firstly, a machine vision system is established to acquire on-site images and perform manual annotation to build the dataset of surface defects on cylinder liner. Secondly, the GSConv SlimNeck mechanism is introduced to reduce the model complexity; the Bi-directional Feature Pyramid Network (BiFPN) is used to fuse the feature information at different scales to enhance the detection accuracy of small surface defects on cylinder liner; and embedding the SimAM attention mechanism to focus on the object region of interest and improve the accuracy and robustness of the model. The final improved YOLOv5 model reduces the number of model parameters by 15.8% compared to the non-improved YOLOv5. And the experimental results on our self-built dataset for cylinder liner defects show that the mAP0.5 is improved by 0.4%. This means that the accuracy of model detection was not compromised. This method can be applied to actual production processes.

Quality evaluation of agricultural and food products is important for processing, inventory control, and marketing. Fruit surface defects are important quality factors for the jujube industry, especially for high quality jujubes such as Xinjiang red jujube. This paper presents the development and test results of a machine vision system for automatic jujube surface defects detection. Unlike other near-infrared spectrometric approaches, the developed machine vision system uses reflective near-infrared image to evaluate jujube quality by analyzing two-dimensional images. Near-infrared image, vision algorithms and a variety of operational details of the system, including cameras, optics, illumination, and fruit carrier are presented. The complete machine vision system has been built, and the experimental results show that the designed machine vision system is feasible to detect the defects of jujubes.

A vision inspection system for the surface defects of strongly reflected metal based on multi-class SVM

The quality of smooth technical surfaces is characterized by the surface roughness and by surface defects. For a stable quality control, objective measuring conditions are necessary. The objective measurement of surface defects especially is an unsolved problem. For the investigation of the scattering characteristics of different surface defects, a measuring device was developed. With this measuring device, the type of defect and the angle dependent scattering intensity were measured and compared with theoretical calculations.

Defect recognition plays an important part in the health monitoring of in-service equipment. Surface defects and sub-surface defects of key components have different effects on the safety of the equipment. Sub-surface defects are difficult to be identified, and it is more likely to cause unpredictable damage than surface defects. However, the existing characteristic cannot accurately identify the defect type. Therefore, it is critical to find features that are useful for defect classification. The fusion of multiple feature values facilitates defect classification. For obtaining multi-feature fusion eigenvalues, this article introduces a fusion matrix based on Fisher's discriminant criterion and correlation analysis, which realizes the fusion of two dimensions of the whole and the local and different aspects. The BP neural network classifier is then used to discriminate and classify surface defects and sub-surface defects. The recognition accuracy of surface defects and sub-surface defects can reach 98.1%. The classification accuracy of this approach is significantly improved compared with the method based on a single feature value and other state-of-the-art methods. The approach can also be used to identify defects with different cross-sectional shapes, indicating that this approach is suited for nature defects.

This work aims at detecting surface defects on reflecting industrial parts. A machine vision system, performing the detection of geometric aspect surface defects, is completely described. The revealing of defects is realized by a particular lighting device. It has been carefully designed to ensure the imaging of defects. The defects segmentation is then straightforward and fast to compute. The imaging conditions have been particularly studied because they influence strongly the quality of acquired images and consequently the quality of image processing results. These imaging conditions are often the fact of experiments: numerous attempts on lighting features and on the relative positions between the cameras, the lighting and the object are still necessary. To bring help in the conception of these imaging conditions, a complete simulation is proposed. The imaging and lighting system has been completely modeled. The simulation, based on computer graphics, enables here the rendering of realistic images. Simulation provides a very efficient way of conception which is applied to the design of a machine vision prototype.

Internal defects detection and classification in hollow cylindrical surfaces using single shot detection and MobileNet

Wildfires have increased in frequency, duration and size in the western United States (U.S.) over the past decades. These trends are projected to continue, with negative consequences for air quality across the U.S. Wildfires emit large quantities of particles and gases that serve as air pollutants and their precursors, and can lead to severe air quality conditions over large spatial and long temporal scales. Characterization of the chemical constituents in smoke as a function of combustion conditions, fuel type, and fuel component is an important step towards improving the prediction of air quality effects from fires and evaluating mitigation strategies. Building on the comprehensive characterization of gaseous non-methane organic compounds (NMOCs) identified in laboratory and field studies, a supervised pattern recognition algorithm was developed that successfully identified unique chemical speciation profiles among similar fuel types common in western coniferous forests. The algorithm was developed using laboratory data from single fuel species and tested on simplified synthetic fuel mixtures. The fuel types in the synthetic mixtures were differentiated but as the relative mixing proportions became more similar, the differentiation became poorer. Using the results from the pattern recognition algorithm, a classification model based on linear discriminant analysis was trained to differentiate smoke samples based on the contribution(s) of dominant fuel type(s). The classification model was applied to field data and despite the complexity of contributing fuels, and the presence of fuels "unknown" to the classifier, the dominant sources/fuel types were identified correctly. The pattern recognition and classification algorithms are a promising approach for identifying the types of fuels contributing to smoke samples and facilitating selection of appropriate chemical speciation profiles for predictive air quality modeling, using a highly reduced suite of measured NMOCs. Utility and performance of the pattern recognition and classification algorithms can be improved by expanding the training and test sets to include data from a broader range of single and mixed fuel types.

The design and study of pulsed eddy current sensors for detecting surface defects in small-diameter rods are highly significant. Accurate detection and identification of surface defects in small-diameter rods may be attained by the ongoing optimization of sensor design and enhancement of detection technologies. This article presents the construction of a non-coaxial differential eddy current sensor (Tx-Rx sensor) and examines the detection of surface defects in a small diameter bar. A COMSOL 3D model is developed to examine the variations in eddy current distribution and defect signal characteristics between the plate and rod components. The position of the excitation coil on the bar and the eddy current disruption around the defect are examined. Additionally, a Tx-Rx sensor has been developed and enhanced concerning coil dimensions, coil separation, and elevation height. An experimental system is established to detect bar structures with surface defects of varying depths, and a model correlating differential signal attenuation with defect depth is proposed, achieving a quantitative relative error of less than 5%, thereby offering a reference for the quantitative detection of bar surface defects.

Advanced upper limb prostheses capable of actuating multiple degrees of freedom (DOFs) are now commercially available. Pattern recognition algorithms that use surface electromyography (EMG) signals show great promise as multi-DOF controllers. Unfortunately, current pattern recognition systems are limited to activate only one DOF at a time. This study introduces a novel classifier based on Bayesian theory to provide classification of simultaneous movements. This approach and two other classification strategies for simultaneous movements were evaluated using nonamputee and amputee subjects classifying up to three DOFs, where any two DOFs could be classified simultaneously. Similar results were found for nonamputee and amputee subjects. The new approach, based on a set of conditional parallel classifiers was the most promising with errors significantly less (p < 0.05) than a single linear discriminant analysis (LDA) classifier or a parallel approach. For three-DOF classification, the conditional parallel approach had error rates of 6.6% on discrete and 10.9% on combined motions, while the single LDA had error rates of 9.4% on discrete and 14.1% on combined motions. The low error rates demonstrated suggest than pattern recognition techniques on surface EMG can be extended to identify simultaneous movements, which could provide more life-like motions for amputees compared to exclusively classifying sequential movements.

Defect classification is an important part of automated surface defect detection systems. Generally, the types of surface defects of industrial products are complex and diverse, and the correct defect type classification can help to subsequently extract the characteristic of defects. Moreover, defect classification can help to count the number of defect modes automatically and provide data support for the precise maintenance of product production. Due to the large number of surface defects and the small difference between defect types, traditional classification methods are difficult to classify defect accurately. Therefore, in order to improve the accuracy of defect image classification, this paper proposes a defect image classification method based on transfer learning and sparse coding. Firstly, a deep CNN feature extraction algorithm for defect images is proposed in combination with transfer learning. Then, the deep CNN features of the defect image are dimension-reduced and sparsely optimized using the sparse coding techniques, and the sparse CNN features are obtained. Finally, the sparse CNN features are classified to realize the defect type determination using the linear SVM. The accuracy of the proposed method is verified by using a steel surface defect image benchmark database, and the effectiveness of the proposed method is proved.

An imbalanced small sample slab defect recognition method based on image generation

QRNet: Query-based reparameterization net for real-time detection of power adapter surface defects