YOLO 的產業應用: 產品瑕疵辨識 / Industrial Application of YOLO: Identifying the Defectiveness of Products
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 improvements across successive versions, enhancing accuracy, performance, and additional functionalities.
有興趣者可了解YOLO演變歷史 Interested researchers are welcomw the history of YOLO:
本研究群也有成功應用其於邊緣運算偵測電鍍品及風機葉片的經驗, Our research group also has successful experience in applying it to edge computing for detecting electroplating products and windturbine blades.
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