Abstract
Abstract: This research provides valuable and compelling insights into the performance comparison of YOLOv8 and YOLOv5 for Thread and No thread detection. The findings underscore the significant impact of architectural improvements and model advancements, resulting in higher precision and accuracy in object detection tasks. Furthermore, the successful implementation of LabelImg and Beautiful Soup demonstrates their remarkable effectiveness in dataset annotation and collection, crucial contributors to the overall success of the study. As a result, these results lay a solid foundation for future advancements in object detection techniques, with wide-ranging applications across diverse domains, encompassing industrial automation, surveillance systems, and beyond.
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