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Scientific Innovation in Asia

Volume 2, 2024 - Issue 1

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Vol. 3 (2025)
Vol. 2 (2024)
Vol. 1 (2023)
Journal Article An open access journal
Open Access Journal Article

RESEARCH ON FRUIT IMAGE PROCESSING CLASSIFICANTION AND RECOGNITION BASED ON RESNET50 NEURAL NETWORK

by Qinghui Xu 1,* ,  Chen Luo 2 ,  Dou Wen 3 ,  Runhua Yang 4 ,  Yilin Wan 2  and  Zongqi Ge 2
1
Faculty of Data Science and Information Technology, INTI International University, Nilai 71800, Negeri Sembilan, Malaysia
2
College of Computer Science and Technology,University of East London,Longbridge Road, Dagenham, Essex RM8 2AS, UK
3
Southwest Jiaotong University Hope College. No.8 Xuefu Avenue, Jintang County, Chengdu City, Sichuan Province, 610400, China.
4
Yunnan Key Laboratory of Service Computing, Yunnan University of Finance and Economics, Kunming, Yunnan 650221, PR China
*
Author to whom correspondence should be addressed.
SIA  2024 2(1):30; https://doi.org/10.12410/sia0201014
Received: 1 December 2024 / Accepted: 12 December 2024 / Published Online: 31 December 2024
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Abstract

Convolutional Neural Network (CNN) is the most widely used algorithm model in computer vision problems , and has achieved remarkable results. In this paper, for the Fruits 360 image dataset on Kaggle, we use the algorithm based on The pre-trained network of Resnet-50, constructs the algorithm to build a convolutional neural network model. The difference between ResNet and traditional convolutional neural networks is that it adds some shortcuts to the network, i.e. direct channels are able to skip certain layers to optimize the network.ResNet introduces the idea of residual function F(x), which is set as: H(x)=F(x)+x. Specifically, if F(x)=0, then the residual functions form an identity map H(x)=x. It is easier for a neural network to fit the residuals.

Keywords: Convolutional Neural Network (CNN); ResNet; Residuals;
Copyright: © 2024 by Xu, Luo, Wen, Yang, Wan and Ge. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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ACS Style
Xu, Q.; Luo, C.; Wen, D.; Yang, R.; Wan, Y.; Ge, Z. RESEARCH ON FRUIT IMAGE PROCESSING CLASSIFICANTION AND RECOGNITION BASED ON RESNET50 NEURAL NETWORK. Scientific Innovation in Asia, 2024, 2, 30. doi:10.12410/sia0201014
AMA Style
Xu Q, Luo C, Wen D et al.. RESEARCH ON FRUIT IMAGE PROCESSING CLASSIFICANTION AND RECOGNITION BASED ON RESNET50 NEURAL NETWORK. Scientific Innovation in Asia; 2024, 2(1):30. doi:10.12410/sia0201014
Chicago/Turabian Style
Xu, Qinghui; Luo, Chen; Wen, Dou; Yang, Runhua; Wan, Yilin; Ge, Zongqi 2024. "RESEARCH ON FRUIT IMAGE PROCESSING CLASSIFICANTION AND RECOGNITION BASED ON RESNET50 NEURAL NETWORK" Scientific Innovation in Asia 2, no.1:30. doi:10.12410/sia0201014

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