一级黄色片免费播放|中国黄色视频播放片|日本三级a|可以直接考播黄片影视免费一级毛片

高級搜索

留言板

尊敬的讀者、作者、審稿人, 關(guān)于本刊的投稿、審稿、編輯和出版的任何問題, 您可以本頁添加留言。我們將盡快給您答復(fù)。謝謝您的支持!

姓名
郵箱
手機(jī)號碼
標(biāo)題
留言內(nèi)容
驗(yàn)證碼

基于可變形卷積神經(jīng)網(wǎng)絡(luò)的遙感影像密集區(qū)域車輛檢測方法

高鑫 李慧 張義 閆夢龍 張宗朔 孫顯 孫皓 于泓峰

高鑫, 李慧, 張義, 閆夢龍, 張宗朔, 孫顯, 孫皓, 于泓峰. 基于可變形卷積神經(jīng)網(wǎng)絡(luò)的遙感影像密集區(qū)域車輛檢測方法[J]. 電子與信息學(xué)報(bào), 2018, 40(12): 2812-2819. doi: 10.11999/JEIT180209
引用本文: 高鑫, 李慧, 張義, 閆夢龍, 張宗朔, 孫顯, 孫皓, 于泓峰. 基于可變形卷積神經(jīng)網(wǎng)絡(luò)的遙感影像密集區(qū)域車輛檢測方法[J]. 電子與信息學(xué)報(bào), 2018, 40(12): 2812-2819. doi: 10.11999/JEIT180209
Xin GAO, Hui LI, Yi ZHANG, Menglong YAN, Zongshuo ZHANG, Xian SUN, Hao SUN, Hongfeng YU. Vehicle Detection in Remote Sensing Images of Dense Areas Based on Deformable Convolution Neural Network[J]. Journal of Electronics & Information Technology, 2018, 40(12): 2812-2819. doi: 10.11999/JEIT180209
Citation: Xin GAO, Hui LI, Yi ZHANG, Menglong YAN, Zongshuo ZHANG, Xian SUN, Hao SUN, Hongfeng YU. Vehicle Detection in Remote Sensing Images of Dense Areas Based on Deformable Convolution Neural Network[J]. Journal of Electronics & Information Technology, 2018, 40(12): 2812-2819. doi: 10.11999/JEIT180209

基于可變形卷積神經(jīng)網(wǎng)絡(luò)的遙感影像密集區(qū)域車輛檢測方法

doi: 10.11999/JEIT180209
基金項(xiàng)目: 國家自然科學(xué)基金(41501485)
詳細(xì)信息
    作者簡介:

    高鑫:男,1966年生,研究員,研究方向?yàn)闄C(jī)載SAR信息處理應(yīng)用、空間信息處理與應(yīng)用系統(tǒng)技術(shù)研究

    李慧:女,1992年生,碩士生,研究方向?yàn)閳D像處理與分析

    張義:男,1987年,助理研究員,研究方向?yàn)殛嚵行盘柼幚?/p>

    閆夢龍:男,1985年生,副研究員,研究方向?yàn)闄C(jī)器學(xué)習(xí)與遙感影像智能解譯

    張宗朔:男,1995年生,本科生

    孫顯:男,1981年生,副研究員,研究方向?yàn)闄C(jī)器學(xué)習(xí)與遙感影像智能解譯

    孫皓:男,1984年生,副研究員,研究方向?yàn)闄C(jī)器學(xué)習(xí)與遙感影像智能解譯

    于泓峰:男,1991年生,助理研究員,研究方向?yàn)闄C(jī)器學(xué)習(xí)與遙感影像智能解譯

    通訊作者:

    李慧  lihuiiecas@163.com

  • 中圖分類號: TP751.2

Vehicle Detection in Remote Sensing Images of Dense Areas Based on Deformable Convolution Neural Network

Funds: The National Natural Science Foundation of China (41501485)
  • 摘要: 車輛檢測是遙感圖像分析領(lǐng)域的熱點(diǎn)研究內(nèi)容之一,車輛目標(biāo)的智能提取和識別,對于交通管理、城市建設(shè)有重要意義。在遙感領(lǐng)域中,現(xiàn)有基于卷積神經(jīng)網(wǎng)絡(luò)的車輛檢測方法存在實(shí)現(xiàn)過程復(fù)雜并且對于車輛密集區(qū)域檢測效果不理想的缺陷。針對上述問題,該文提出基于端到端的神經(jīng)網(wǎng)絡(luò)模型DF-RCNN以提高車輛密集區(qū)域的檢測精度。首先,在特征提取階段,DF-RCNN模型將深淺層特征圖的分辨率統(tǒng)一并融合;其次,DF-RCNN模型結(jié)合可變形卷積和可變形感興趣區(qū)池化模塊,通過加入少量的參數(shù)和計(jì)算量以學(xué)習(xí)目標(biāo)的幾何形變。實(shí)驗(yàn)結(jié)果表明,該文提出的模型針對密集區(qū)域的車輛目標(biāo)具有較好的檢測性能。
  • 圖  1  Faster-CNN結(jié)構(gòu)圖

    圖  2  DF-RCNN模型結(jié)構(gòu)

    圖  3  融合特征圖結(jié)構(gòu)

    圖  4  可變性卷積和可變形RoI池化模塊學(xué)習(xí)過程

    圖  5  融合1, 3, 5層特征圖和結(jié)合可變形模塊的檢測結(jié)果

    圖  6  不同模型的檢測結(jié)果

    表  1  3 種尺度和 3 種比例映射候選區(qū)域尺寸

    區(qū)域尺寸和比例 202, 1:1 202, 1:2 202, 2:1 302, 1:1 302, 1:2 302, 2:1 402, 1:1 402, 1:2 402, 2:1
    映射區(qū)域大小 20×20 14×28 28×14 30×30 21×42 42×21 40×40 28×56 57×28
    下載: 導(dǎo)出CSV

    表  2  不同層檢測性能比較

    模型 檢測區(qū)域 召回率 準(zhǔn)確率 F1指標(biāo)
    層1 密集區(qū)域 0.461 0.611 0.525
    層3 密集區(qū)域 0.623 0.754 0.682
    層5 密集區(qū)域 0.655 0.923 0.766
    層3+5 密集區(qū)域 0.714 0.930 0.808
    層2+4 密集區(qū)域 0.709 0.925 0.803
    層1+3+5 密集區(qū)域 0.725 0.927 0.814
    下載: 導(dǎo)出CSV

    表  3  結(jié)合可變形模塊檢測性能比較

    模型 檢測區(qū)域 召回率 準(zhǔn)確率 F1指標(biāo)
    層1+3+5,可變形卷積 密集區(qū)域 0.731 0.932 0.819
    層1+3+5,可變形RoI池化 密集區(qū)域 0.726 0.925 0.814
    層1+3+5,可變形卷積,可變形RoI池化 密集區(qū)域 0.744 0.940 0.831
    層5,可變形卷積,可變形RoI池化 密集區(qū)域 0.725 0.924 0.812
    層2+4,可變形卷積,可變形RoI池化 密集區(qū)域 0.715 0.922 0.805
    層3+5,可變形卷積,可變形RoI池化 密集區(qū)域 0.729 0.928 0.817
    下載: 導(dǎo)出CSV

    表  4  不同模型檢測性能比較

    模型 檢測區(qū)域 召回率 準(zhǔn)確率 F1指標(biāo)
    Faster-RCNN 密集區(qū)域 0.655 0.932 0.766
    DCNN 密集區(qū)域 0.402 0.925 0.560
    DF-RCNN 密集區(qū)域 0.744 0.940 0.831
    Faster-RCNN 非密集區(qū)域 0.901 0.942 0.921
    DCNN 非密集區(qū)域 0.962 0.785 0.865
    DF-RCNN 非密集區(qū)域 0.910 0.952 0.931
    下載: 導(dǎo)出CSV
  • SHAO Wen, YANG Wen, LIU Guang, et al. Car detection from high-resolution aerial imagery using multiple features[C]. Geoscience and Remote Sensing Symposium, Munich, Germany, 2012: 4379–4382.
    MORANDUZZO T and MELGANI F. Automatic car counting method for unmanned aerial vehicle images[J]. IEEE Transactions on Geoscience&Remote Sensing, 2013, 52(3): 1635–1647 doi: 10.1109/TGRS.2013.2253108
    KLUCKNER S, PACHER G, GRABNER H, et al. A 3D teacher for car detection in aerial images[C]. International Conference on Computer Vision, Rio de Janeiro, Brazil, 2007: 1–8.
    TUERMER S, KURZ F, Reinartz P, et al. Airborne vehicle detection in dense urban areas using HoG features and disparity maps[J]. IEEE Journal of Selected Topics in Applied Earth Observations&Remote Sensing, 2013, 6(6): 2327–2337 doi: 10.1109/JSTARS.2013.2242846
    CHEN Ziyi, WANG Cheng, WEN Chenglu, et al. Vehicle detection in high-resolution aerial images via sparse representation and superpixels[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 54(1): 103–116 doi: 10.1109/TGRS.2015.2451002
    康妙, 計(jì)科峰, 冷祥光, 等. 基于棧式自編碼器特征融合的SAR圖像車輛目標(biāo)識別[J]. 雷達(dá)學(xué)報(bào), 2017, 6(2): 167–176 doi: 10.12000/JR16112

    KANG Miao, JI Kefeng, LENG Xiangguang, et al. SAR target recognition with feature fusion based on stacked autoencoder[J]. Journal of Radars, 2017, 6(2): 167–176 doi: 10.12000/JR16112
    王思雨, 高鑫, 孫皓, 等. 基于卷積神經(jīng)網(wǎng)絡(luò)的高分辨率SAR圖像飛機(jī)目標(biāo)檢測方法[J]. 雷達(dá)學(xué)報(bào), 2017, 6(2): 195–203 doi: 10.12000/JR17009

    WANG Siyu, GAO Xin, SUN Hao, et al. An aircraft detection method based on convolutional neural networks in high-resolution SAR images[J]. Journal of Radars, 2017, 6(2): 195–203 doi: 10.12000/JR17009
    田壯壯, 占榮輝, 胡杰民, 等. 基于卷積神經(jīng)網(wǎng)絡(luò)的SAR圖像目標(biāo)識別研究[J]. 雷達(dá)學(xué)報(bào), 2016, 5(3): 320–325 doi: 10.12000/JR16037

    TIAN Zhuangzhuang, ZHAN Ronghui, HU Jiemin, et al. SAR ATR based on convolutional neural network[J]. Journal of Radars, 2016, 5(3): 320–325 doi: 10.12000/JR16037
    YANG Zi and PUN-CHENG L S C. Vehicle detection in intelligent transportation systems and its applications under varying environments: A review[J]. Image&Vision Computing, 2017, 69: 143–154 doi: 10.1016/j.imavis.2017.09.008
    CHEN Xueyun, XIANG Shiming, LIU Chenglin, et al. Vehicle detection in satellite images by hybrid deep convolutional neural networks[J]. IEEE Geoscience&Remote Sensing Letters, 2014, 11(10): 1797–1801 doi: 10.1109/LGRS.2014.2309695
    LI Hao, FU Kun, YAN Menglong, et al. Vehicle detection in remote sensing images using denoizing-based convolutional neural networks[J]. Remote Sensing Letters, 2017, 8(3): 262–270 doi: 10.1080/2150704X.2016.1258127
    REN Shaoqing, HE Kaiming, SUN Jian, et al. Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis&Machine Intelligence, 2017, 39(6): 1137–1149 doi: 10.1109/TPAMI.2016.2577031
    DENG Zhipeng, SUN Hao, ZHOU Shilin, et al. Toward fast and accurate vehicle detection in aerial images using coupled region-based convolutional neural networks[J]. IEEE Journal of Selected Topics in Applied Earth Observations&Remote Sensing, 2017, 10(8): 3652–3664 doi: 10.1109/JSTARS.2017.2694890
    ELMIKATY M and STATHAKI T. Car detection in aerial images of dense urban areas[J]. IEEE Transactions on Aerospace&Electronic Systems, 2017, 54(1): 51–63 doi: 10.1109/TAES.2017.2732832
    DAI Jifeng, QI Haozhi, XIONG Yunwen, et al. Deformable convolutional networks[OL]. http://arxiv.org/abs/1703.06211.2017.
    LIU Derong, LI Hongliang, and WANG Ding. Feature selection and feature learning for high-dimensional batch reinforcement learning: A survey[J]. International Journal of Automation and Computing, 2015, 12(3): 229–242 doi: 10.1007/s11633-015-0893-y
    YANG Bin, YAN Junjie, LEI Zhen, et al. Convolutional channel features[C]. IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 82–90.
    ZHONG Bin, ZHANG Jun, WANG Pengfei, et al. Jointly feature learning and selection for robust tracking via a gating mechanism[J]. Plos One, 2016, 11(8): e0161808 doi: 10.1371/journal.pone.0161808
    SIMONYAN K and ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[OL]. http://arxiv.org/abs/1409.1556.2014.
    HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, United States, 2016: 770–778.
    ZEILER M D and FERGUS R.Visualizing and Understanding Convolutional Networks[C]. European Conference on Computer Vision, Zurich, Switzerland, 2014: 818–833.
  • 加載中
圖(6) / 表(4)
計(jì)量
  • 文章訪問數(shù):  2248
  • HTML全文瀏覽量:  1012
  • PDF下載量:  130
  • 被引次數(shù): 0
出版歷程
  • 收稿日期:  2018-03-02
  • 修回日期:  2018-06-11
  • 網(wǎng)絡(luò)出版日期:  2018-07-16
  • 刊出日期:  2018-12-01

目錄

    /

    返回文章
    返回