Object detection

Abstract

Object detection has become a cornerstone of modern computer vision, with applications spanning diverse industries. Convolutional Neural Networks (CNNs) have played a transformative role in this domain, enabling automated systems to extract hierarchical features from images for tasks like classification and object detection. Building on CNNs, the R-CNN family introduced a two-stage approach that combined region proposal techniques with CNN feature extraction, setting a benchmark for object detection accuracy. Further advancements, such as YOLO (You Only Look Once), revolutionized the field by treating object detection as a single regression problem, achieving real-time performance with remarkable speed and competitive accuracy. This project explores the evolution of object detection algorithms, starting with CNNs and R-CNN, progressing to the first YOLO algorithm, and culminating in the latest YOLOv11. Leveraging the YOLO algorithm to develop an efficient inventory tracking system. By utilizing YOLOv11’s advanced detection capabilities, the system ensures accurate object localization and identification, meeting industrial standards for quality management. This innovative approach will significantly enhance inventory processes, allowing for better tracking and management of resources