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顯著性背景感知的多尺度紅外行人檢測(cè)方法

趙斌 王春平 付強(qiáng)

趙斌, 王春平, 付強(qiáng). 顯著性背景感知的多尺度紅外行人檢測(cè)方法[J]. 電子與信息學(xué)報(bào), 2020, 42(10): 2524-2532. doi: 10.11999/JEIT190761
引用本文: 趙斌, 王春平, 付強(qiáng). 顯著性背景感知的多尺度紅外行人檢測(cè)方法[J]. 電子與信息學(xué)報(bào), 2020, 42(10): 2524-2532. doi: 10.11999/JEIT190761
Bin ZHAO, Chunping WANG, Qiang FU. Multi-scale Pedestrian Detection in Infrared Images with Salient Background-awareness[J]. Journal of Electronics & Information Technology, 2020, 42(10): 2524-2532. doi: 10.11999/JEIT190761
Citation: Bin ZHAO, Chunping WANG, Qiang FU. Multi-scale Pedestrian Detection in Infrared Images with Salient Background-awareness[J]. Journal of Electronics & Information Technology, 2020, 42(10): 2524-2532. doi: 10.11999/JEIT190761

顯著性背景感知的多尺度紅外行人檢測(cè)方法

doi: 10.11999/JEIT190761
詳細(xì)信息
    作者簡(jiǎn)介:

    趙斌:男,1990年生,博士生,研究方向?yàn)樯疃葘W(xué)習(xí)、目標(biāo)檢測(cè)

    王春平:男,1965年生,博士生導(dǎo)師,研究方向?yàn)閳D像處理、火力控制理論與應(yīng)用

    付強(qiáng):男,1981年生,講師,博士,研究方向?yàn)橛?jì)算機(jī)視覺(jué)、網(wǎng)絡(luò)化火控與指控技術(shù)

    通訊作者:

    王春平 wang_c_p@163.com

  • 中圖分類(lèi)號(hào): TN215

Multi-scale Pedestrian Detection in Infrared Images with Salient Background-awareness

  • 摘要: 超大視場(chǎng)(U-FOV)紅外成像系統(tǒng)探測(cè)范圍大、不受光照限制,但存在尺度多樣、小目標(biāo)豐富的特點(diǎn)。為此該文提出一種具備背景感知能力的多尺度紅外行人檢測(cè)方法,在提高小目標(biāo)檢測(cè)性能的同時(shí),減少冗余計(jì)算。首先,構(gòu)建了4尺度的特征金字塔網(wǎng)絡(luò)分別獨(dú)立預(yù)測(cè)目標(biāo),補(bǔ)充高分辨率細(xì)節(jié)特征。其次,在特征金字塔結(jié)構(gòu)的橫向連接中融入注意力模塊,產(chǎn)生顯著性特征,抑制不相關(guān)區(qū)域的特征響應(yīng)、突出圖像局部目標(biāo)特征。最后,在顯著性系數(shù)的基礎(chǔ)上構(gòu)建了錨框掩膜生成子網(wǎng)絡(luò),約束錨框位置,排除平坦背景,提高處理效率。實(shí)驗(yàn)結(jié)果表明,顯著性生成子網(wǎng)絡(luò)僅增加5.94%的處理時(shí)間,具備輕量特性;超大視場(chǎng)(U-FOV)紅外行人數(shù)據(jù)集上的識(shí)別準(zhǔn)確率達(dá)到了93.20%,比YOLOv3高了26.49%;錨框約束策略能節(jié)約處理時(shí)間18.05%。重構(gòu)模型具有輕量性和高準(zhǔn)確性,適合于檢測(cè)超大視場(chǎng)中的多尺度紅外目標(biāo)。
  • 圖  1  超大視場(chǎng)紅外圖像行人特性

    圖  2  多尺度紅外行人檢測(cè)網(wǎng)絡(luò)結(jié)構(gòu)

    圖  3  注意力模塊結(jié)構(gòu)

    圖  4  顯著性特征與卷積特征融合方法

    圖  5  錨框掩膜生成過(guò)程

    圖  6  不同輸入圖像的錨框掩膜

    圖  7  不同二值化閾值下的錨框掩膜

    圖  8  紅外行人檢測(cè)可視化結(jié)果

    表  1  不同IoU閾值下的行人檢測(cè)平均準(zhǔn)確率

    方法主干網(wǎng)絡(luò)訓(xùn)練集平均準(zhǔn)確率(AP)
    IoU=0.3IoU=0.45IoU=0.5IoU=0.7
    Faster R-CNNResNet101U-FOV0.5932
    SSDMobilenet_v1U-FOV0.5584
    R-FCNResNet101U-FOV0.6312
    CSPResnet50U-FOV0.8414
    YOLOv3Darknet53U-FOV0.65950.66710.66280.6461
    YOLOv3+FSDarknet53U-FOV0.88800.88700.88280.8511
    YOLOv3+FSDarknet53Caltech+U-FOV0.90570.90780.90840.8961
    本文方法Darknet53Caltech+U-FOV0.92010.93200.93150.9107
    下載: 導(dǎo)出CSV

    表  2  參數(shù)量對(duì)比

    方法總參數(shù)量可訓(xùn)練參數(shù)量不可訓(xùn)練參數(shù)量
    YOLOv3615763426152373452608
    本文方法648619766480629655680
    下載: 導(dǎo)出CSV

    表  3  U-FOV測(cè)試集圖像總處理時(shí)間及處理幀速

    方法YOLOv3YOLOv3+AttentionFS+Attention本文方法
    總時(shí)間(s)90.3595.72125.39107.25
    處理幀率7.326.915.276.16
    下載: 導(dǎo)出CSV
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  • 收稿日期:  2019-09-30
  • 修回日期:  2020-05-13
  • 網(wǎng)絡(luò)出版日期:  2020-05-20
  • 刊出日期:  2020-10-13

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