Learning manufacturing computer vision systems using tiny YOLOv4

作者：Adán Medina, Russel Bradley, Wenhao Xu, Pedro Ponce, Brian Anthony, Arturo Molina · 发表于：Frontiers in Robotics and AI · 年份：2024 · DOI：10.3389/frobt.2024.1331249 · 被引用次数：4 · 研究领域：Industrial Vision Systems and Defect Detection、Advanced Neural Network Applications、CCD and CMOS Imaging Sensors

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...