Machine vision significantly improves the efficiency, quality, and reliability of defect detection. In visual inspection, excellent optical illumination platforms and suitable image acquisition hardware are the prerequisites for obtaining high-quality images. Image processing and analysis are key technologies in obtaining defect information, while deep learning is significantly impacting the field of image analysis. In this study, a brief history and the state of the art in optical illumination, image acquisition, image processing, and image analysis in the field of visual inspection are systematically discussed. The latest developments in industrial defect detection based on machine vision are introduced. In the further development of the field of visual inspection, the application of deep learning will play an increasingly important role. Thus, a detailed description of the application of deep learning in defect classification, localization and segmentation follows the discussion of traditional defect detection algorithms. Finally, future prospects for the development of visual inspection technology are explored.

/pdf/state-of-the-art-in-defect-detection-based-on-machine-vision-2rtzvgsnqy.pdf

State of the Art in Defect Detection Based on Machine Vision

In the lumber and wood processing industry, most visual quality inspections are still done by trained human operators. Visual inspection is a tedious and repetitive task that involves a high likelihood of human error. Currently, new automated solutions with high-resolution cameras and visual inspection algorithms are being tested, but they are not always fast and accurate enough for real-time industrial applications. This paper proposes an automatic visual inspection system for the location and classification of defects on the wood surface. We adopted a faster region-based convolutional neural network (faster R-CNN) for the identification of defects on wood veneer surfaces. Faster R-CNN has been successfully used in medical image processing and object tracking before, but it has not yet been applied for wood panel surface quality assurance. To improve the results, we used pre-trained AlexNet, VGG16, BNInception, and ResNet152 neural network models for transfer learning. The results of the experiments using a synthetically augmented dataset are presented. The best average accuracy of 80.6% was obtained using the pretrained ResNet152 neural network model. By combining all the defect classes, a 96.1% accuracy of finding wood panel surface defects was achieved.

Automated Identification of Wood Veneer Surface Defects Using Faster Region-Based Convolutional Neural Network with Data Augmentation and Transfer Learning

Deep learning for medical image segmentation: State-of-the-art advancements and challenges

Automatic dimensional defect detection for glass vials based on machine vision: A heuristic segmentation method

The wood industry is facing many challenges. The high variability of raw material and the complexity of manufacturing processes results in a wide range of visible structure defects, which have to be controlled by trained specialists. These manual processes are not only tedious and biased, but also less effective. To overcome the drawbacks of the manual quality control processes, several automated vision-based systems have been proposed. Even though some conducted studies achieved a higher recognition rate than trained experts, researchers have to deal with a lack of large-scale databases and authentic data in this field. To address this issue, we performed a data acquisition experiment set in the industrial environment, where we were able to acquire an extensive set of authentic data from a production line. For this purpose, we designed and implemented a complex technical solution suitable for high-speed acquisition during harsh manufacturing conditions. In this data note, we present a large-scale dataset of high-resolution sawn timber surface images containing more than 43 000 labelled surface defects and covering 10 types of the most common wood defects. Moreover, with each image record, we provide two types of labels allowing researchers to perform semantic segmentation, as well as defect classification, and localization.

/pdf/a-large-scale-image-dataset-of-wood-surface-defects-for-2pa6fqoijy.pdf

A large-scale image dataset of wood surface defects for automated vision-based quality control processes

Citrus disease detection and classification using based on convolution deep neural network

Automatic defect classification methods are important to increase the productivity of the forest industry. In this respect, classification is also an important component of a pattern recognition system. Well designated classification algorithm will make recognition process more efficient and productive. Quality control is one of the most important steps among the applications that use classification. There are various techniques which are available in order to check quality of wooden material. However, display based quality control of wooden materials is still stands as a hard process. Although there are quality control methods, there are quality control problems because of the classification depending on the person’s eyestrain. We aim the reduction of labor costs. Also, we aim to achieve a more accurate and reliable way to make automatic quality classification. In this article, we proposed a method for quality control of wood material. In order to determine quality control of wooden material, knot detection algorithm which is developed using image processing techniques. This knot detection algorithm consists of several steps. These steps are morphological preprocesses in the knot preprocessing step, knot features obtained from Wavelet Moment (WM) in the feature extraction step, k nearest neighbor method (KNN) classification technique in the classification step. DOI: http://dx.doi.org/10.5755/j01.eie.22.6.17227

/pdf/classification-of-knot-defect-types-using-wavelets-and-knn-ppnq78akb7.pdf

Classification of Knot Defect Types Using Wavelets and KNN

Segmentation of brain tumors is of great importance for patients in clinical diagnosis and treatment. For this reason, experts try to identify border regions of special importance using multimodal images from magnetic resonance imaging systems. In some images, border regions may be intertwined. As a result, this situation leads experts to make incomplete or wrong decisions. This paper presents DenseUNet+, a new deep learning-based approach to perform segmentation with high accuracy using multimodal images. In the DenseUNet+ model, data from four different modalities were used together in dense block structures. Afterward, linear operations were applied to these data and then the concatenate operation was performed. The results obtained in this way were transferred to the decoder layer. The proposed method was also compared with state-of-the-art (SOTA) studies using the same dataset by using dice and jaccard metrics in the BraTS2021 and FeTS2021 datasets. As a result of the comparison, dice and jaccard evaluation metrics for the BraTS2021 dataset were 95% and 88%, respectively, and 86% and 87% performance values were obtained for FeTS2021, respectively. It has been determined that the performance results are better than many SOTA brain tumor segmentation methods. 

DenseUNet+: A novel hybrid segmentation approach based on multi-modality images for brain tumor segmentation

Cataract is one of the most serious eye diseases that can blind if left untreated. Detection of the disease in the early stages rather than in the advanced stages can prevent the patient from being blind. At this point, suspected patients should be constantly checked. Continuous control and follow-up of patients is a tiring and laborious process. For the reasons stated, two different deep learning models are proposed in this article that can be used in the diagnosis and detection of cataracts to assist the work and procedures of ophthalmologists. The proposed deep learning models were run on a fundus dataset with normal and cataract symptoms. The proposed deep learning models provide automatic classification of normal and cataract images. Fine-tuning and layer additions were performed on the upper layer using a pre-trained deep learning model called MobileNet V3 Small. A basic MobileNet V3 Small model has also been created to evaluate the performance of the model, which has been enriched by fine-tuning and adding layers to its upper layers. The difference between the proposed model and the basic model is demonstrated by comparing the classification performances of cataract and normal images with accuracy and complexity matrix measurements. According to the best results obtained in the performance comparisons made by separating the training and test data according to the KFold option, the proposed model gave a more successful result graph of 8.26% than the basic model. Finally, the proposed MobileNet V3 model has also been tested on images composed of two different datasets. On average, the proposed MobileNet V3 model on the combined dataset reached 96.62% accuracy.

Cataract disease classification from fundus images with transfer learning based deep learning model on two ocular disease datasets

In this article, it is aimed to categorize meaningful content from uncontrolled growing written social sharing data using natural language processing. Uncategorized data can disturb social sharing users with an increasing user network due to deprecating and negative content. For the stated reason, a hybrid model based on CNN and LSTM has been proposed to automatically classify all written social sharing content, both positive and negative, into defined target tags. With the proposed hybrid model, it is aimed at automatically classifying the content of the social sharing system into different categories by using the simplest embedding layer, keras. As a result of the experimental studies carried out, a better result was obtained than in the different studies in the literature using the same data set with the proposed method. The obtained performance results show that the proposed method can be applied to different multilabel text analysis problems.

/pdf/cnn-ve-lstm-tabanli-hibrit-bir-derin-ogrenme-modeli-ile-cok-l4ifk7ed.pdf

Cnn ve lstm tabanli hi̇bri̇t bi̇r deri̇n öğrenme modeli̇ i̇le çok eti̇ketli̇ meti̇n anali̇zi̇

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