YOLO (You Only Look Once) 是一系列即時物件偵測的機器學習演算法。物件偵測是電腦視覺的基本任務,利用神經網路來識別和分類影像中的物件。這項技術的應用範圍非常廣泛,包括醫療成像、自動車和監控系統。在用於物體偵測的各種機器學習方法中,卷積神經網路 (CNN) 扮演了舉足輕重的角色。 CNN 是所有 YOLO 模型的基礎,可讓研究人員與工程師有效率地執行物件偵測與分割任務。作為開放原始碼的模型,YOLO 已經在該領域中獲得廣泛採用,並在相繼的版本中不斷改進,提高了精確度、性能和附加功能。 YOLO (You Only Look Once) is a family of real-time object detection machine learning algorithms. Object detection, a fundamental task in computer vision, leverages neural networks to identify and classify objects within images. This technology has a broad range of applications, including medical imaging, autonomous vehicles, and surveillance systems. Among the various machine learning approaches used for object detection, convolutional neural networks (CNNs) play a pivotal role. CNNs serve as the foundation for all YOLO models, enabling researchers and engineers to perform object detection and segmentation tasks efficiently. As open-source models, YOLO has gained widespread adoption in the field, with continuous improvemen...