Automatsko otkrivanje oštećenja na tradicionalnim drvenim konstrukcijama metodom klasifikacije slika utemeljenom na dubokom učenju

Wood has a long history of being used as a valuable resource when it comes to building materials. Due to various external factors, in particular the weather, wood is liable to progressive damage over time, which negatively impacts the endurance of wooden structures. Damage assessment is key in understanding, as well as in effectively mitigating, problems that wooden structures are likely to face. The use of a classification system, via deep learning, can potentially reduce the probability of damage in engineering projects reliant on wood. The present study employed a transfer learning technique, to achieve greater accuracy, and instead of training a model from scratch, to determine the likelihood of risks to wooden structures prior to project commencement. Pretrained MobileNet_V2, Inception_V3, and ResNet_V2_50 models were used to customize and initialize weights. A separate set of images, not shown to the trained model, was used to examine the robustness of the models. The three models were compared in their abilities to assess the possibilities and types of damage. Results revealed that all three models achieve performance rates of similar reliability. However, when considering the loss ratios in regard to efficiency, it became apparent that the multi-layered MobileNet_V2 classifier stood out as the most effective of the pre-trained deep convolutional neural network (CNN) models.Drvo kao vrijedan građevni materijal ima dugu povijest uporabe u graditeljstvu. No zbog brojnih vanjskih čimbenika, posebice vremenskih utjecaja, drvo tijekom vremena postaje podložno progresivnom propadanju, što negativno utječe na izdržljivost drvenih konstrukcija. Procjena šteta na drvu ključna je za razumijevanje problema koji će vjerojatno nastati na drvenim konstrukcijama, kao i za njihovo učinkovito ublažavanje. Primjena sustava klasifikacije uz pomoć dubokog učenja može potencijalno smanjiti vjerojatnost oštećenja u inženjerskim projektima koji se oslanjaju na drvo. U ovom je istraživanju primijenjena tehnika transfernog učenja kako bi se postigla veća točnost modela umjesto da se model za utvrđivanje vjerojatnosti rizika za drvene konstrukcije radi prije početka projekta. Za prilagodbu i inicijalizaciju težina primijenjeni su unaprijed osposobljeni modeli MobileNet_V2, Inception_V3 i ResNet_V2_50. Za ispitivanje robusnosti modela upotrijebljen je zaseban skup slika koji nije prikazan u osposobljenome modelu. Spomenuta tri modela uspoređena su s obzirom na njihove mogućnosti procjene vjerojatnosti i vrste oštećenja drvenih konstrukcija. Rezultati su otkrili da sva tri modela imaju sličnu pouzdanost. Međutim, kada se uzmu u obzir omjeri gubitaka u odnosu prema učinkovitosti, postalo je očito da se višeslojni MobileNet_V2 klasifikator istaknuo kao najučinkovitiji od unaprijed pripremljenih modela dubokih konvolucijskih neuronskih mreža (CNN)

Rail Robot for Rail Track Inspection

Railway transportation requires constant inspections and immediate maintenance to ensure public safety. Traditional manual inspections are not only time consuming, but also expensive. In addition, the accuracy of defect detection is also subjected to human expertise and efficiency at the time of inspection. Computing and Robotics offer automated IoT based solutions where robots could be deployed on rail-tracks and hard to reach areas, and controlled from control rooms to provide faster and low-cost inspection. In this thesis, a novel automated system based on robotics and visual inspection is proposed. The system provides local image processing while inspecting and cloud storage of information that consist of images of the defected railway tracks only. The proposed system utilizes state of the art Machine Learning system and applies it on the images obtained from the tracks in order to classify them as normal or suspicious. Such locations are then marked and more careful inspection can be performed by a dedicated operator with very few locations to inspect (as opposed to the full track)

Identification of wood defect using pattern recognition technique

This study proposed a classification model for timber defect classification based on an artificial neural network (ANN). Besides that, the research also focuses on determining the appropriate parameters for the neural network model in optimizing the defect identification performance, such as the number of hidden layers nodes and the number of epochs in the neural network. The neural network's performance is compared with other standard classifiers such as Naïve Bayes, K-Nearest Neighbours, and J48 Decision Tree in finding their significant differences across the multiple timber species. The classifier's performance is measured based on the F-measure due to the imbalanced dataset of the timber species. The experimental results show that the proposed classification model based on the neural network outperforms the other standard classifiers in detecting many types of defects across multiple timber species with an F-measure of 84.01%. This research demonstrates that ANN can accurately classify the defects across multiple species while defining appropriate parameters (hidden layers and epochs) for the neural network model in optimizing defect identification performance

Automated Identification of Wood Surface Defects Based on Deep Learning

Wood plates are widely used in the interior design of houses primarily for their aesthetic value. However, considering its esthetical values, surface defect detection is necessary. The development of computer vision and CNN-based object detection methods has opened the way for wood surface defect detection process automation. This paper investigates deep-learning applications for automatic wood surface defect detection. It includes the evaluation of deep learning algorithms, including data generation and labeling, preprocessing, model training, and evaluation. Many adjustments regarding the dataset size, the model, and the modification of the neural network were made to evaluate the model's performance in the specified challenge. The results indicate that modifications can increase the YOLOv5s performance in detection. The model with GCNet added and trained in 4800 images has achieved 88.1% of mAP. The paper also evaluates the time performance of models based on different GPU units. The results show that in A100 40GB GPU, the maximum time to process a wood plate is 2.2 seconds. Finally, an Active learning approach for the continual increase in performance while detecting with the smaller size of manual labeling has been implemented. After detecting 500 images in 5 cycles, the model achieved 98.8% of mAP. This scientific paper concludes that YOLOv5s modified model is suitable for wood surface defect detection. It can perform with high accuracy in real time. Moreover, applying the active learning approach can facilitate the labeling process by increasing the performance during detection

Anomaly Detection in the Molecular Structure of Gallium Arsenide Using Convolutional Neural Networks

This paper concerns the development of a machine learning tool to detect anomalies in the molecular structure of Gallium Arsenide. We employ a combination of a CNN and a PCA reconstruction to create the model, using real images taken with an electron microscope in training and testing. The methodology developed allows for the creation of a defect detection model, without any labeled images of defects being required for training. The model performed well on all tests under the established assumptions, allowing for reliable anomaly detection. To the best of our knowledge, such methods are not currently available in the open literature; thus, this work fills a gap in current capabilities