首页|Data from School of Engineering and Sciences Update Knowledge in Robotics and Ar tificial Intelligence (Learning manufacturing computer vision systems using tiny YOLOv4)
Data from School of Engineering and Sciences Update Knowledge in Robotics and Ar tificial Intelligence (Learning manufacturing computer vision systems using tiny YOLOv4)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on robotics and artifici al intelligence have been presented. According to news originating from the Scho ol of Engineering and Sciences by NewsRx editors, the research stated, "Implemen ting and deploying advanced technologies are principal in improving manufacturin g processes, signifying a transformative stride in the industrial sector. Comput er vision plays a crucial innovation role during this technological advancement, demonstrating broad applicability and profound impact across various industrial operations. This pivotal technology is not merely an additive enhancement but a revolutionary approach that redefines quality control, automation, and operatio nal efficiency parameters in manufacturing landscapes." Our news reporters obtained a quote from the research from School of Engineering and Sciences: "By integrating computer vision, industries are positioned to opt imize their current processes significantly and spearhead innovations that could set new standards for future industrial endeavors. However, the integration of computer vision in these contexts necessitates comprehensive training programs f or operators, given this advanced system's complexity and abstract nature. Histo rically, training modalities have grappled with the complexities of understandin g concepts as advanced as computer vision. Despite these challenges, computer vi sion has recently surged to the forefront across various disciplines, attributed to its versatility and superior performance, often matching or exceeding the ca pabilities of other established technologies. Nonetheless, there is a noticeable knowledge gap among students, particularly in comprehending the application of Artificial Intelligence (AI) within Computer Vision. This disconnect underscores the need for an educational paradigm transcending traditional theoretical instr uction. Cultivating a more practical understanding of the symbiotic relationship between AI and computer vision is essential. To address this, the current work proposes a project-based instructional approach to bridge the educational divide . This methodology will enable students to engage directly with the practical as pects of computer vision applications within AI. By guiding students through a h ands-on project, they will learn how to effectively utilize a dataset, train an object detection model, and implement it within a microcomputer infrastructure. This immersive experience is intended to bolster theoretical knowledge and provi de a practical understanding of deploying AI techniques within computer vision. The main goal is to equip students with a robust skill set that translates into practical acumen, preparing a competent workforce to navigate and innovate in th e complex landscape of Industry 4.0."
School of Engineering and SciencesMach ine LearningRobotics and Artificial IntelligenceTechnology
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