Vision-based Defect Inspection Research Articles

An Efficient YOLO Algorithm with an Attention Mechanism for Vision-Based Defect Inspection Deployed on FPGA.

Industry 4.0 features intelligent manufacturing. Among them, the vision-based defect inspection algorithm is remarkable for quality control in parts manufacturing. With the help of AI and machine learning, auto-adaptive instead of manual operation is achievable in this field, and much progress has been made in recent years. In this study, considering the demand of inspection features in industrialization, we made further improvement in smart defect inspection. An efficient algorithm using Field Programmable Gate Array (FPGA)-accelerated You Only Look Once (YOLO) v3 based on an attention mechanism is proposed. First, because of the relatively fixed camera angle and defect features, an attention mechanism based on the concept of directing the focus of defect inspection is proposed. The attention mechanism consists of three improvements: (a) image preprocessing, which is to tailor images for selectively concentrating on the defect relevant things. Image preprocessing mainly includes cutting, zooming and splicing, named CZS operations. (b) Tailoring the YOLOv3 backbone network, which is to ignore invalid inspection regions in deep neural networks and optimize the network structure. (c) Data augmentation. First, two improvements can be made to efficiently reduce deep learning operations and accelerate the inspection speed, but the preprocessed images are similar and the lack of diversity will reduce network accuracy. So, (c) is added to mitigate the lack of considerable amounts of training data. Second, the algorithm is deployed on a PYNQ-Z2 FPGA board to meet the industrialization production requirements for accuracy, efficiency and extensibility. FPGA can provide a low-latency, low-cost, high-power-efficiency and flexible architecture that enables deep learning acceleration for industrial scenarios. A Xilinx Deep Neural Network Development Kit (DNNDK) converted the improved YOLOv3 to Programmable Logic (PL), which can be deployed on FPGA. The conversion process mainly consists of pruning, quantization and compilation. Experimental results showed that the algorithm had high efficiency, inspection accuracy reached 99.2%, processing speed reached 1.54 Frames per Second (FPS), and power consumption was only 10 W.

Vision-Based Defect Inspection and Condition Assessment for Sewer Pipes: A Comprehensive Survey.

Due to the advantages of economics, safety, and efficiency, vision-based analysis techniques have recently gained conspicuous advancements, enabling them to be extensively applied for autonomous constructions. Although numerous studies regarding the defect inspection and condition assessment in underground sewer pipelines have presently emerged, we still lack a thorough and comprehensive survey of the latest developments. This survey presents a systematical taxonomy of diverse sewer inspection algorithms, which are sorted into three categories that include defect classification, defect detection, and defect segmentation. After reviewing the related sewer defect inspection studies for the past 22 years, the main research trends are organized and discussed in detail according to the proposed technical taxonomy. In addition, different datasets and the evaluation metrics used in the cited literature are described and explained. Furthermore, the performances of the state-of-the-art methods are reported from the aspects of processing accuracy and speed.

Automated defect inspection system for metal surfaces based on deep learning and data augmentation

Recent efforts to create a smart factory have inspired research that analyzes process data collected from Internet of Things (IOT) sensors, to predict product quality in real time. This requires an automatic defect inspection system that quantifies product quality data by detecting and classifying defects in real time. In this study, we propose a vision-based defect inspection system to inspect metal surface defects. In recent years, deep convolutional neural networks (DCNNs) have been used in many manufacturing industries and have demonstrated the excellent performance as a defect classification method. A sufficient amount of training data must be acquired, to ensure high performance using a DCNN. However, owing to the nature of the metal manufacturing industry, it is difficult to obtain enough data because some defects occur rarely. Owing to this imbalanced data problem, the generalization performance of the DCNN-based classification algorithm is lowered. In this study, we propose a new convolutional variational autoencoder (CVAE) and deep CNN-based defect classification algorithm to solve this problem. The CVAE-based data generation technology generates sufficient defect data to train the classification model. A conditional CVAE (CCVAE) is proposed to generate images for each defect type in a single CVAE model. We also propose a classifier based on a DCNN with high generalization performance using data generated from the CCVAE. In order to verify the performance of the proposed method, we performed experiments using defect images obtained from an actual metal production line. The results showed that the proposed method exhibited an excellent performance.

Improved illumination for vision-based defect inspection of highly reflective metal surface

Specular and strong reflections are the main problems encountered during part image defect inspection of shiny or highly reflective surfaces. In this letter, we propose an improved illumination method for defect inspection. A diffuse light source is designed based on the physics analysis of light reflection. The distribution of intensity is simulated according to a known model to verify the illumination uniformity of the source. Experiments show that defect expressivity when using the proposed illumination method has a better performance. The optical model is not only suitable for the defect detection of metal balls but also for the defect detection of planes and cylinders.

Real-time vision-based defect inspection for high-speed steel products

In the steel-making industry, both the quality and quantity of the products are critical. This work presents a real-time defect detection method for high-speed steel bar in coil (BIC). For good performance characteristics, the detection algorithm must be robust to problems associated with the cylindrical shape of BICs, the presence of noise and nonuniform brightness distribution of images, the various types of defects, and so on. Furthermore, because the target speed is very high, it should have a fast processing time. Therefore, a defect detection algorithm should satisfy the two conflicting requirements of reducing the processing time and improving the efficiency of defect detection. This work proposes an effective real-time defect detection algorithm that can satisfy these conditions. Moreover, to reduce cost, the proposed algorithm is implemented on a PC-based real-time defect detection system without a professional digital signal processing (DSP) board. Experimental results show that the proposed algorithm guarantees both real-time processing and accurate detection.

New horizon for CNN: with fuzzy paradigms for multimedia

The cellular neural network (CNN) is a powerful technique to mimic the local function of biological neural circuits for real-time image and video processing. Recently, it is widely accepted that using a set of CNNs in parallel can achieve higher-level information processing and reasoning functions either from application or biology points of views. The authors introduce a novel framework for constructing a multiple-CNN integrated neural system called recurrent fuzzy CNN (RFCNN). This system can automatically learn its proper network structure and parameters simultaneously. In the RFCNN, each learned fuzzy rule corresponds to a CNN. Hence, each CNN takes care of a fuzzily separated problem region, and the functions of all CNNs are integrated through the fuzzy inference mechanism. Some online clustering algorithms are introduced for the structure learning, and the ordered-derivative calculus is applied to derive the recurrent learning rules of CNN templates in the parameter-learning phase. RFCNN provides a solution to the current dilemma on the decision of templates and/or fuzzy rules in the existing integrated (fuzzy) CNN systems. The capability of the RFCNN is demonstrated on the real-world vision-based defect inspection and image descreening problems proving that the RFCNN scheme is effective and promising.