Learning manufacturing computer vision systems using tiny YOLOv4

• Published in: Frontiers in robotics and AI Vol. 11; p. 1331249
• Main Authors: Medina, Adan, Bradley, Russel, Xu, Wenhao, Ponce, Pedro, Anthony, Brian, Molina, Arturo
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 12.06.2024
• Subjects: education; educational innovation; higher education; manufacturing; object detection; quality control; Robotics and AI; educational innovation; education; Tiny YOLO v4; quality control; computer vision; manufacturing; higher education; object detection
• ISSN: 2296-9144; 2296-9144
• DOI: 10.3389/frobt.2024.1331249

Implementing and deploying advanced technologies are principal in improving manufacturing processes, signifying a transformative stride in the industrial sector. Computer 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 operational efficiency parameters in manufacturing landscapes. By integrating computer vision, industries are positioned to optimize 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 for operators, given this advanced system’s complexity and abstract nature. Historically, training modalities have grappled with the complexities of understanding concepts as advanced as computer vision. Despite these challenges, computer vision has recently surged to the forefront across various disciplines, attributed to its versatility and superior performance, often matching or exceeding the capabilities 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 instruction. 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 aspects of computer vision applications within AI. By guiding students through a hands-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 provide 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 the complex landscape of Industry 4.0. This approach emphasizes the criticality of adapting educational strategies to meet the evolving demands of advanced technological infrastructures. It ensures that emerging professionals are adept at harnessing the potential of transformative tools like computer vision in industrial settings.