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基于區(qū)域與深度殘差網(wǎng)絡(luò)的圖像語義分割

羅會(huì)蘭 盧飛 孔繁勝

羅會(huì)蘭, 盧飛, 孔繁勝. 基于區(qū)域與深度殘差網(wǎng)絡(luò)的圖像語義分割[J]. 電子與信息學(xué)報(bào), 2019, 41(11): 2777-2786. doi: 10.11999/JEIT190056
引用本文: 羅會(huì)蘭, 盧飛, 孔繁勝. 基于區(qū)域與深度殘差網(wǎng)絡(luò)的圖像語義分割[J]. 電子與信息學(xué)報(bào), 2019, 41(11): 2777-2786. doi: 10.11999/JEIT190056
Huilan LUO, Fei LU, Fansheng KONG. Image Semantic Segmentation Based on Region and Deep Residual Network[J]. Journal of Electronics & Information Technology, 2019, 41(11): 2777-2786. doi: 10.11999/JEIT190056
Citation: Huilan LUO, Fei LU, Fansheng KONG. Image Semantic Segmentation Based on Region and Deep Residual Network[J]. Journal of Electronics & Information Technology, 2019, 41(11): 2777-2786. doi: 10.11999/JEIT190056

基于區(qū)域與深度殘差網(wǎng)絡(luò)的圖像語義分割

doi: 10.11999/JEIT190056
基金項(xiàng)目: 國(guó)家自然科學(xué)基金(61862031, 61462035),江西省自然科學(xué)基金(20171BAB202014)
詳細(xì)信息
    作者簡(jiǎn)介:

    羅會(huì)蘭:女,1974年生,博士,教授,研究方向?yàn)闄C(jī)器學(xué)習(xí)和模式識(shí)別等

    盧飛:男,1994年生,碩士,研究方向?yàn)閳D像語義分割

    孔繁勝:男,1946年生,博士生導(dǎo)師,教授,研究方向人工智能與知識(shí)發(fā)現(xiàn)等

    通訊作者:

    羅會(huì)蘭 luohuilan@sina.com

  • 中圖分類號(hào): TP391.41

Image Semantic Segmentation Based on Region and Deep Residual Network

Funds: The National Natural Science Foundation of China (61862031, 61462035), The Natural Science Foundation of Jiangxi Province (20171BAB202014)
  • 摘要: 該文提出了一種結(jié)合區(qū)域和深度殘差網(wǎng)絡(luò)的語義分割模型?;趨^(qū)域的語義分割方法使用多尺度提取相互重疊的區(qū)域,可識(shí)別多種尺度的目標(biāo)并得到精細(xì)的物體分割邊界?;谌矸e網(wǎng)絡(luò)的方法使用卷積神經(jīng)網(wǎng)絡(luò)(CNN)自主學(xué)習(xí)特征,可以針對(duì)逐像素分類任務(wù)進(jìn)行端到端訓(xùn)練,但是這種方法通常會(huì)產(chǎn)生粗糙的分割邊界。該文將兩種方法的優(yōu)點(diǎn)結(jié)合起來:首先使用區(qū)域生成網(wǎng)絡(luò)在圖像中生成候選區(qū)域,然后將圖像通過帶擴(kuò)張卷積的深度殘差網(wǎng)絡(luò)進(jìn)行特征提取得到特征圖,結(jié)合候選區(qū)域以及特征圖得到區(qū)域的特征,并將其映射到區(qū)域中每個(gè)像素上;最后使用全局平均池化層進(jìn)行逐像素分類。該文還使用了多模型融合的方法,在相同的網(wǎng)絡(luò)模型中設(shè)置不同的輸入進(jìn)行訓(xùn)練得到多個(gè)模型,然后在分類層進(jìn)行特征融合,得到最終的分割結(jié)果。在SIFT FLOW和PASCAL Context數(shù)據(jù)集上的實(shí)驗(yàn)結(jié)果表明該文方法具有較高的平均準(zhǔn)確率。
  • 圖  1  本文所提模型框架

    圖  2  帶擴(kuò)張卷積的卷積神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)

    圖  3  全局平均池化層結(jié)構(gòu)示意圖

    圖  4  模型融合框架示意圖

    圖  5  SIFT FLOW數(shù)據(jù)集上圖像分割效果

    圖  6  PASCAL Context數(shù)據(jù)集上圖像分割效果

    表  1  本文算法與其他先進(jìn)方法在SIFT FLOW數(shù)據(jù)集上的實(shí)驗(yàn)對(duì)比(%)

    方法平均準(zhǔn)確率(MA)像素準(zhǔn)確率(PA)
    Yang[21]48.7079.80
    Long[7]51.7085.20
    Eigen[22]55.7086.80
    Caesar[15]64.0084.30
    本文算法66.2085.70
    下載: 導(dǎo)出CSV

    表  2  本文算法與其他先進(jìn)方法在PASCAL Context數(shù)據(jù)集上的實(shí)驗(yàn)對(duì)比(%)

    方法平均準(zhǔn)確率(MA)像素準(zhǔn)確率(PA)平均交并比(MIoU)
    O2P[3]18.10
    Dai[23]34.40
    Long[7]46.5065.9035.10
    Caesar[15]49.9062.4032.50
    本文52.2066.3034.70
    下載: 導(dǎo)出CSV

    表  3  3種不同擴(kuò)張卷積核使用方案的性能比較

    實(shí)驗(yàn)操作 最后卷積層輸出大小SIFT FLOW MA(%)
    1無操作19×1964.50
    2僅移除stride操作僅Res4 (stride=1)38×3826.61
    3僅Res5 (stride=1)38×3837.47
    4Res4 (stride=1)+Res5 (stride=1)75×7539.76
    5+設(shè)置dilated僅Res4(dilated=2)38×3864.20
    6僅Res5(dilated=4)38×3863.60
    7Res4(dilated=2)+Res5(dilated=4)75×7565.50
    下載: 導(dǎo)出CSV

    表  4  4個(gè)單模型以及融合模型在SIFT FLOW上的效果比較

    模型序號(hào)候選區(qū)域尺寸SIFT FLOW MA(%)
    17×764.20
    29×964.80
    313×1365.30
    415×1565.20
    融合模型3 465.70
    融合模型3 466.00
    融合模型1 2 3 466.20
    下載: 導(dǎo)出CSV
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  • 收稿日期:  2019-01-18
  • 修回日期:  2019-04-05
  • 網(wǎng)絡(luò)出版日期:  2019-04-22
  • 刊出日期:  2019-11-01

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