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The task of utilizing machine vision for the detection of casting surface defects is characterized by small targets, real-time performance, and ease of mobility. The direct application of current mainstream object detection networks for defect detection presents issues of low accuracy and efficiency. Consequently, in this paper, we introduce Casting-DETR, an end-to-end network designed for casting surface defect detection. To assess and validate the model's performance, 554 images of casting samples with surface defects were employed. Casting-DETR achieved an impressive detection rate of 98.97% on the test set, with a single image detection time of 91.5ms. Furthermore, a real-time detection system, built using PyQT6, was tested in four different environments. Casting-DETR exhibited exceptional performance, maintaining a single-frame detection time of approximately 90 ms, demonstrating the model's high robustness and suitability for real-time detection. The Casting-DETR network proposed in this paper is an end-to-end solution with rapid convergence, superior detection accuracy, and swift detection speeds, offering a fresh perspective for similar detection tasks within the industry. [ABSTRACT FROM AUTHOR]

Sign language recognition is one of the fundamental ways to assist deaf people to communicate with others. An accurate vision-based sign language recognition system using deep learning is a fundamental goal for many researchers. Deep convolutional neural networks have been extensively considered in the last few years, and a slew of architectures have been proposed. Recently, Vision Transformer and other Transformers have shown apparent advantages in object recognition compared to traditional computer vision models such as Faster R-CNN, YOLO, SSD, and other deep learning models. In this paper, we propose a Vision Transformer-based sign language recognition method called DETR (Detection Transformer), aiming to improve the current state-of-the-art sign language recognition accuracy. The DETR method proposed in this paper is able to recognize sign language from digital videos with a high accuracy using a new deep learning model ResNet152 + FPN (i.e., Feature Pyramid Network), which is based on Detection Transformer. Our experiments show that the method has excellent potential for improving sign language recognition accuracy. For instance, our newly proposed net ResNet152 + FPN is able to enhance the detection accuracy up to 1.70% on the test dataset of sign language compared to the standard Detection Transformer models. Besides, an overall accuracy 96.45% was attained by using the proposed method. [ABSTRACT FROM AUTHOR]

Detection of objects and their location in an image are important elements of current research in computer vision. In May 2020, Meta released its state-of-the-art object-detection model based on a transformer architecture called detection transformer (DETR). There are several object-detection models such as region-based convolutional neural network (R-CNN), you only look once (YOLO) and single shot detectors (SSD), but none have used a transformer to accomplish this task. These models mentioned earlier, use all sorts of hyperparameters and layers. However, the advantages of using a transformer pattern make the architecture simple and easy to implement. In this paper, we determine the name of a chemical experiment through two steps: firstly, by building a DETR model, trained on a customized dataset, and then integrate it into an augmented reality mobile application. By detecting the objects used during the realization of an experiment, we can predict the name of the experiment using a multi-class classification approach. The combination of various computer vision techniques with augmented reality is indeed promising and offers a better user experience. [ABSTRACT FROM AUTHOR]