基于卷积神经网络的螺栓松动检测技术研究进展

Zhenzhu Guo, Yiduo Zhang
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引用次数: 1

摘要

卷积神经网络在提取图像深层特征方面具有强大的泛化和表达能力。它们的出现进一步推动了人工智能的发展,大大提高了图像识别检测效果和计算机运行速度。随着全球智能时代的到来,基于卷积神经网络的图像识别与检测技术已经出现在工件检测的各个领域,同时也给该领域的专业人员带来了优化智能算法的挑战。本文对卷积神经网络的识别过程以及卷积神经网络在螺栓松动图像识别中的研究现状进行了描述和展望。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research Progress Of Bolt Loose Detection Technology Based On Convolutional Neural Network
Convolutional neural networks have powerful generalization and expression capabilities for extracting deep-level features of images. Their emergence has further promoted the development of artificial intelligence, and greatly improved the image recognition and detection effects and computer operating speed. With the advent of the global intelligent era, image recognition and detection technology based on convolutional neural networks has emerged in various fields of workpiece detection, and it has also brought challenges for professionals in this field to optimize intelligent algorithms. In this paper, the recognition process of the convolutional neural network and the current research status of the convolutional neural network in the recognition of bolt looseness images are described and prospected.
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