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Detection of Defects in Bottled Liquor Using Deep Learning

Published: 26 December 2023 Publication History

Abstract

In this paper, we aim at the problem of low detection accuracy in bottle defect detection; combined with the characteristics of the bottle defect dataset, this paper adopts the method of data enhancement to enable the model to be trained through a small amount of information, improve the model's ability to capture defects, and thus improve the generalization ability of the model. Secondly, the algorithm is improved for the false detection and missed detection problems in the defect detection of wine bottles. Due to its unique construction method, the feature pyramid network can fuse the high-level feature information to improve detection accuracy. The study used the concept of feature pyramid network (FPN) and applied ResNet-65 network is used as the backbone network structure. Through experimental verification, this method can improve detection accuracy.

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  1. Detection of Defects in Bottled Liquor Using Deep Learning

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    WSSE '23: Proceedings of the 2023 5th World Symposium on Software Engineering
    September 2023
    352 pages
    ISBN:9798400708053
    DOI:10.1145/3631991
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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    Published: 26 December 2023

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    Author Tags

    1. Feature Pyramid Network
    2. Network model optimization
    3. Raster R-CNN
    4. Surface defects of Bottled Liquor

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