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

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

高鑫¹,
李慧^{1, 2, ,},
張義¹,
閆夢龍¹,
張宗朔³,
孫顯¹,
孫皓¹,
于泓峰¹

1.
中國科學(xué)院電子學(xué)研究所空間信息處理與應(yīng)用系統(tǒng)技術(shù)重點(diǎn)實(shí)驗(yàn)室 ??北京 ??100190
2.
中國科學(xué)院大學(xué) ??北京 ??100049
3.
中央蘭開夏大學(xué) ??英國 ??PR1 2HE

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
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- 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

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

1.
Key Laboratory of Technology in Geo-spatial Information Processing and Application System, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China
3.
University of Central Lancashire (UCLan), Preston PR1 2HE, United Kingdom

Funds: The National Natural Science Foundation of China (41501485)

摘要

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。
- 遙感影像 /
- 車輛檢測 /
- 密集區(qū)域 /
- 端到端卷積神經(jīng)網(wǎng)絡(luò)
Abstract: Vehicle detection is one of the hotspots in the field of remote sensing image analysis. The intelligent extraction and identification of vehicles are of great significance to traffic management and urban construction. In remote sensing field, the existing methods of vehicle detection based on Convolution Neural Network (CNN) are complicated and most of these methods have poor performance for dense areas. To solve above problems, an end-to-end neural network model named DF-RCNN is presented to solve the detecting difficulty in dense areas. Firstly, the model unifies the resolution of the deep and shallow feature maps and combines them. After that, the deformable convolution and RoI pooling are used to study the geometrical deformation of the target by adding a small number of parameters and calculations. Experimental results show that the proposed model has good detection performance for vehicle targets in dense areas.
- Remote sensing images /
- Vehicle detection /
- Dense areas /
- End-to-end convolution neural network

HTML全文

圖 1 Faster-CNN結(jié)構(gòu)圖

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圖 2 DF-RCNN模型結(jié)構(gòu)

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圖 3 融合特征圖結(jié)構(gòu)

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圖 4 可變性卷積和可變形RoI池化模塊學(xué)習(xí)過程

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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

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圖 6 不同模型的檢測結(jié)果

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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

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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

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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

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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

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