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基于改進(jìn)ResNet-50的圖像特征提取網(wǎng)絡(luò )
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南京工程學(xué)院 人工智能產(chǎn)業(yè)技術(shù)研究院

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TP391.41

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國家自然科學(xué)基金青年基金資助項目(61903183)


Image feature extraction network based on improved ResNet-50
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    摘要:

    為了提高圖像的特征質(zhì)量,保證最后提取到的特征高度精煉,提出了一種新的方法;該方法首先將低分辨率圖像經(jīng)過(guò)小波變換分解成高頻分量和低頻分量,并結合插值法進(jìn)行插值,最后通過(guò)小波逆變換得到高分辨率圖像來(lái)為后續的特征提取提供高質(zhì)量的圖片輸入;接著(zhù),選取ResNet-50網(wǎng)絡(luò )作為基礎網(wǎng)絡(luò ),將Efficient Channel Attention(ECA)模塊與ResNet殘差結構結合形成一個(gè)全新的ECA-ResNet50模塊,ECA模塊具有的通道級的注意力機制,可以讓整個(gè)網(wǎng)絡(luò )更加專(zhuān)注于提取顯著(zhù)特征;經(jīng)實(shí)驗測試,該方法對于圖像特征提取的質(zhì)量有著(zhù)明顯的提升,均方誤差下降可達6.65;結果表明,該方法可行有效,具有良好的工程應用前景;

    Abstract:

    In order to improve the quality of image features and ensure that the final extracted features are highly refined, a new method is proposed;Firstly, the low resolution image is decomposed into high frequency component and low frequency component by wavelet transform, and interpolated by interpolation method;Finally, the high resolution image is obtained by inverse wavelet transform to provide high quality image input for subsequent feature extraction;Then, ResNet-50 network is selected as the basic network, and Efficient Channel Attention(ECA) module is combined with ResNet residual structure to form a new ECA-Resnet50 module; ECA module has a channel-level attention mechanism, which can make the whole network more focused on extracting salient features;Experimental results show that this method can significantly improve the quality of image feature extraction, and the mean square error can be reduced up to 6.65; The results show that the method is feasible and effective, and has a good engineering application prospect;

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湯博宇,焦良葆,徐逸,孟琳.基于改進(jìn)ResNet-50的圖像特征提取網(wǎng)絡(luò )計算機測量與控制[J].,2023,31(6):162-167.

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歷史
  • 收稿日期:2022-10-17
  • 最后修改日期:2022-11-17
  • 錄用日期:2022-11-18
  • 在線(xiàn)發(fā)布日期: 2023-06-15
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