Hyper Parameter Optimization for Transfer Learning of ShuffleNetV2 with Edge Computing for Casting Defect Detection

A casting defect is an expendable abnormality and the most undesirable thing in the metal casting process. In Casting Defect Detection, deep learning based on Convolution Neural Network (CNN) models has been widely used, but most of these models require a lot of processing power. This work proposes a low-power ShuffleNet V2-based Transfer Learning model for defect identification with low latency, easy upgrading, increased efficiency, and an automatic visual inspection system with edge computing. Initially, various image transformation techniques were used for data augmentation on casting datasets to test the model flexibility in diverse casting. Subsequently, a pre-trained lightweight ShuffleNetV2 model is adapted, and hyperparameters are fine-tuned to optimize the model. The work results in a lightweight, adaptive, and scalable model ideal for resource-constrained edge devices. Finally, the trained model can be used as an edge device on the NVIDIA Jetson Nano-kit to speed up detection. The measures of precision, recall, accuracy, and F1 score were utilized for model evaluation. According to the statistical measures, the model accuracy is 99.58%, precision is 100%, recall is 99%, and the F1-Score is 100 %

Development Of A Computed Radiography-Based Weld Defect Detection And Classification System [RC78.7.D35 K75 2008 f rb].

Dalam penyelidikan ini, satu sistem bersepadu yang terdiri daripada satu peta kecacatan dan satu pengelas pelbagai rangkaian neural bagi peruasan, pengesanan dan pengesanan kecacatan kimpalan telah direkabentuk dan dibangun. In this research, an integrated system consisting of a flaw map and a multiple neural network classifier for weld defect segmentation, detection, and classification is designed and developed

Detection, 3-D positioning, and sizing of small pore defects using digital radiography and tracking

This article presents an algorithm that handles the detection, positioning, and sizing of submillimeter-sized pores in welds using radiographic inspection and tracking. The possibility to detect, position, and size pores which have a low contrast-to-noise ratio increases the value of the nondestructive evaluation of welds by facilitating fatigue life predictions with lower uncertainty. In this article, a multiple hypothesis tracker with an extended Kalman filter is used to track an unknown number of pore indications in a sequence of radiographs as an object is rotated. Each pore is not required to be detected in all radiographs. In addition, in the tracking step, three-dimensional (3-D) positions of pore defects are calculated. To optimize, set up, and pre-evaluate the algorithm, the article explores a design of experimental approach in combination with synthetic radiographs of titanium laser welds containing pore defects. The pre-evaluation on synthetic radiographs at industrially reasonable contrast-to-noise ratios indicate less than 1% false detection rates at high detection rates and less than 0.1 mm of positioning errors for more than 90% of the pores. A comparison between experimental results of the presented algorithm and a computerized tomography reference measurement shows qualitatively good agreement in the 3-D positions of approximately 0.1-mm diameter pores in 5-mm-thick Ti-6242

Nondestructive Testing in Composite Materials

In this era of technological progress and given the need for welfare and safety, everything that is manufactured and maintained must comply with such needs. We would all like to live in a safe house that will not collapse on us. We would all like to walk on a safe road and never see a chasm open in front of us. We would all like to cross a bridge and reach the other side safely. We all would like to feel safe and secure when taking a plane, ship, train, or using any equipment. All this may be possible with the adoption of adequate manufacturing processes, with non-destructive inspection of final parts and monitoring during the in-service life of components. Above all, maintenance should be imperative. This requires effective non-destructive testing techniques and procedures. This Special Issue is a collection of some of the latest research in these areas, aiming to highlight new ideas and ways to deal with challenging issues worldwide. Different types of materials and structures are considered, different non-destructive testing techniques are employed with new approaches for data treatment proposed as well as numerical simulations. This can serve as food for thought for the community involved in the inspection of materials and structures as well as condition monitoring

Robot Detector de Enfermedades en Cultivos de Gulupa

Se detallará el proceso de diseño y simulación de un Robot capaz de transitar a lo largo de un cultivo de gulupa detectando por medio de un algoritmo las gulupas presentes en el cultivo filtrando cada una de forma individual, además de una aplicación móvil que se comunica con el robot por medio de un servidor donde comparten las imágenes de los frutos, estas imágenes son mostradas en la aplicación dentro de una tabla que contiene información de una imagen. el diseño del robot se hizo en el software SolidWorks 2017, dicho modelo CAD fue exportado a Blender, para posteriormente ser cargado en el software de simulación GAZEBO para utilizar el framework ROS, esto con el fin de simular el movimiento del robot y desarrollar diversas pruebas de desempeño al dispositivo como el paso por obstáculos, visualización de objetos y correcto funcionamiento de los sensores. El algoritmo para la detección de frutos se basará en el uso de diversas técnicas de filtrado como convolución y watershet, utilizando las librerías para el lenguaje de programación Python: OpenCV y Numpy, para evaluar el rendimiento del algoritmo se hará con una base de datos que contiene 2090 imágenes de frutos tomadas en diversos estados de iluminación y posición del fruto. Al procesar cada imagen con el algoritmo se extraerán los falsos positivos y verdaderos positivos, con estos datos se calculará la efectividad que tuvo el algoritmo. La aplicación de dispositivo móvil se programará en el entorno de programación Android Studio y el lenguaje kotlin, dicha aplicación descargará la información almacenada en un servidor que será creado usando las funciones de apache en este será cargada una base de datos en la cual se utilizaran las herramientas MySQL y PhpMyAdmin, el desempeño de esta aplicación y servidor será medido cargando imágenes de salida del algoritmo en la base de datos y que sean visualizados en la aplicación.The design and simulation process of a robot capable of moving along a gulupa crop will be detailed, detecting by means of an algorithm the gulupas present in the crop, filtering each one individually, as well as a mobile application that communicates with the robot through a server where they share the images of the fruits, these images are shown in the application within a table that contains information about an image. The robot design was made in the SolidWorks 2017 software, said CAD model was exported to Blender, to later be loaded into the GAZEBO simulation software to use the ROS framework, this to simulate the movement of the robot and develop various tests. of performance to the device such as the passage through obstacles, visualization of objects and correct operation of the sensors. The algorithm for the detection of fruits will be based on the use of various filtering techniques such as convolution and watershet, using the libraries for the Python programming language: OpenCV and Numpy, to evaluate the performance of the algorithm it will be done with a database that contains 2090 images of fruits taken in various lighting states and position of the fruit. When processing each image with the algorithm, the false positives and true positives will be extracted, with these data the effectiveness of the algorithm will be calculated. The mobile device application will be programmed in the Android Studio programming environment and the kotlin language, said application will download the information stored on a server that will be created using the Apache functions, in which a database will be loaded in which the functions will be used. MySQL and PhpMyAdmin tools, the performance of this application and server will be measured by uploading output images of the algorithm to the database and displaying them in the application

LASER RANGE IMAGING FOR ON-LINE MAPPING OF 3D IMAGES TO PSEUDO-X-RAY IMAGES FOR POULTRY BONE FRAGMENT DETECTION

A laser ranging image system was developed for on-line high-resolution 3D shape recovery of poultry fillets. The range imaging system in conjunction with X-ray imaging was used to provide synergistic imaging detection of bone fragments in poultry fillets. In this research, two 5 mW diode lasers coupled with two CCD cameras were used to produce 3D information based on structured lights and triangulation. A laser scattering phenomenon on meat tissues was studied when calculating the object thickness. To obtain the accurate 3D information, the cameras were calibrated to correct for camera distortions. For pixel registrations of the X-ray and laser 3D images, the range imaging system was calibrated, and noises and signal variations in the X-ray and laser 3D images were analyzed. Furthermore, the relationship between the X-ray absorption and 3D thickness of fillets was obtained, and a mapping function based on this relationship was applied to convert the fillet 3D images into the pseudo-X-ray images. For the on-line system implementation, the imaging hardware and software engineering issues, including the data flow optimization and the operating system task scheduling, were also studied. Based on the experimental on-line test, the range imaging system developed was able to scan poultry fillets at a speed of 0.2 m/sec at a resolution of 0.8(X) x 0.7(Y) x 0.7(Z) mm3. The results of this study have shown great potential for non-invasive detection of hazardous materials in boneless poultry meat with uneven thickness