Research on Image Recognition Method Based on Deep Learning Algorithm

Aizezi, Yasen; Jiamali, Anniwaer; Abudurexiti, Ruxianguli; Liu, Xuehua; Du, Ji-Xiang; Ding, Liping

doi:10.1109/avss.2018.8639372

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…[12], which are widely used in speech recognition, image processing, etc. [13]. This paper chooses the YOLOv4 algorithm from the YOLO series algorithms.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning: A Study of Pattern Recognition for Personalized Clothing

Zhao,

Zhu,

Liu

2023

HighTech. Innov. J.

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Objectives: This article aims to enhance the efficiency of clothing recognition and retrieval by implementing deep learning algorithms for personalized clothing pattern recognition. Methods: Based on the you only look once version 4 (YOLOv4) algorithm in deep learning, the CSPDarknet-53 in the original algorithm was replaced by GhostNet, and the original Leaky ReLU activation function was replaced by FMish. Then, an improved YOLOv4 algorithm was obtained. Experiments were carried out on the personalized clothing pattern set, the Fashion Mnist dataset, and the DeepFashion dataset to compare and analyze different algorithms. Findings: When replacing CSPDarknet-53 with GhostNet and the Leaky ReLU activation function with FMish, the optimized YOLOv4 algorithm performed significantly better, verifying the reliability of the YOLOv4 improvement. The optimized algorithm achieved an F1 value of 94.22% and a mAP of 95.41% on different datasets, and 39.51% and 49.56% on the DeepFashion dataset, respectively, outperforming other deep learning methods such as the faster-recurrent convolutional neural network. Furthermore, the floating-point operations per second of the optimized YOLOv4 algorithm were 8.72 G, showing a reduction of 49.71% compared to the traditional algorithm. This suggested that it had low complexity and calculation amounts. Novelty: The optimized YOLOv4 algorithm performs excellently in recognizing personalized clothing patterns, which can provide a new and reliable approach for recognition and retrieval in the field of clothing. Doi: 10.28991/HIJ-2023-04-03-03 Full Text: PDF

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“…[12], which are widely used in speech recognition, image processing, etc. [13]. This paper chooses the YOLOv4 algorithm from the YOLO series algorithms.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning: A Study of Pattern Recognition for Personalized Clothing

Zhao,

Zhu,

Liu

2023

HighTech. Innov. J.

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Research on Image Classification Algorithm Based on Convolutional Neural Network

Luo¹

2021

J. Phys.: Conf. Ser.

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Nowadays, we are in the information age. Pictures carry a lot of information and play an indispensable role. For a large number of images, it is very important to find useful image information within the effective time. Therefore, the excellent performance of the image classification algorithm has certain influence factors on the result of image classification. Image classification is to input an image, and then use a certain classification algorithm to determine the category of the image. The main process of image classification: image preprocessing, image feature extraction and classifier design. Compared with the manual feature extraction of traditional machine learning, the convolutional neural network under the deep learning model can automatically extract local features and share weights. Compared with traditional machine learning algorithms, the image classification effect is better. This paper focuses on the study of image classification algorithms based on convolutional neural networks, and at the same time compares and analyzes deep belief network algorithms, and summarizes the application characteristics of different algorithms.

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Research on Image Recognition Method Based on Deep Learning Algorithm

Cited by 2 publications

References 3 publications

Deep Learning: A Study of Pattern Recognition for Personalized Clothing

Deep Learning: A Study of Pattern Recognition for Personalized Clothing

Research on Image Classification Algorithm Based on Convolutional Neural Network

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