We participated in Taiwan's National Intercollegiate Athletic Games (NIAG) in early May 2021, and formed a Sport Technology Team of more than 30 scholars, students, and engineers to provide novel systems and solutions that make the athletic games rich in sport technologies. Some of the features could be the first time shown to the Internet audience for large-scale athletic games. The technologies involved include table tennis ball trajectory and bounce distribution, badminton shuttlecock tracking and trajectory, augmented-reality enriched video streaming on social networks, real-time three-dimensional broadcasting with free view-angle, in-stadium video stream pushing by a private fifth-generation (5G) network with multiaccess edge computing, AI-based sport data analytics during live streaming, etc. All the technologies and applications are integrated in a novel technology-enhanced broadcasting system (TEBS) that is dedicated to sport events. This article introduces the respective technologies that we have developed, deployed, and demonstrated in the 2021 NIAG. We stress the layered architecture design and integration of the TEBS, as well as experimental results from real games in the smart stadium and swimming pool. We also discuss the technical challenges and our approaches to tackle them, as well as lessons learned.

The fiducial-marks-based alignment process is one of the most critical steps in printed circuit board (PCB) manufacturing. In the alignment process, a machine vision technique is used to detect the fiducial marks and then adjust the position of the vision system in such a way that it is aligned with the PCB. The present study proposed an embedded PCB alignment system, in which a rotation, scale and translation (RST) template-matching algorithm was employed to locate the marks on the PCB surface. The coordinates and angles of the detected marks were then compared with the reference values which were set by users, and the difference between them was used to adjust the position of the vision system accordingly. To improve the positioning accuracy, the angle and location matching process was performed in refinement processes. To overcome the matching time, in the present study we accelerated the rotation matching by eliminating the weak features in the scanning process and converting the normalized cross correlation (NCC) formula to a sum of products. Moreover, the scanning time was reduced by implementing the entire RST process in parallel on threads of a graphics processing unit (GPU) by applying hash functions to find refined positions in the refinement matching process. The experimental results showed that the resulting matching time was around 32× faster than that achieved on a conventional central processing unit (CPU) for a test image size of 1280 × 960 pixels. Furthermore, the precision of the alignment process achieved a considerable result with a tolerance of 36.4 μm.

This letter proposes a lightweight DNN model for robotic grasping applications with just 1.5 million architecture parameters. In the proposed model, the location of the target object is estimated using a pairwise template matching method, while the orientation of the object is predicted using depth images and a convolutional neural network (CNN). The feasibility of the proposed model is demonstrated both numerically and experimentally on a NVIDIA Jetson NX developer kit. The experimental results show that the grasping system achieves an accuracy of 96.3% and a running time of 125 ms when on 700 images. Moreover, when applied to practical grasping tasks on 20 unseen objects selected from the Cornell grasping dataset, the system achieves an accuracy of 92.5%, which is comparable to that of existing state-of-the-art methods reported in the literature.

Nuts are the cornerstone of human industrial construction, especially A-grade nuts that can only be used in power plants, precision instruments, aircraft, and rockets. However, the traditional nuts inspection method is to manually operate the measuring instrument for conducting an inspection, so the quality of the A-grade nut cannot be guaranteed. In this work, a machine vision-based inspection system was proposed, which performs a real-time geometric inspection of the nuts before and after tapping on the production line. In order to automatically screen out A-Grade nuts on the production line, there are 7 inspections within this proposed nut inspection system. The measurements of parallel, opposite side length, straightness, radius, roundness, concentricity, and eccentricity were proposed. To shorten the overall detection time regarding nut production, the program needed to be accurate and uncomplicated. By modifying the Hough line and Hough circle, the algorithm became faster and more suitable for nut detection. The optimized Hough line and Hough circle can be used for all measures in the testing process.

In Industry 4.0, automation is a critical requirement for mechanical production. This study proposes a computer vision-based method to capture images of rotating tools and detect defects without the need to stop the machine in question. The study uses frontal lighting to capture images of the rotating tools and employs scale-invariant feature transform (SIFT) to identify features of the tool images. Random sample consensus (RANSAC) is then used to obtain homography information, allowing us to stitch the images together. The modified YOLOv4 algorithm is then applied to the stitched image to detect any surface defects on the tool. The entire tool image is divided into multiple patch images, and each patch image is detected separately. The results show that the modified YOLOv4 algorithm has a recall rate of 98.7% and a precision rate of 97.3%, and the defect detection process takes approximately 7.6 s to complete for each stitched image.