基于非局部操作的深度卷積神經(jīng)網(wǎng)絡(luò)車位占用檢測算法

申鉉京; 沈哲; 黃永平; 王玉

doi:10.11999/JEIT190349

基于非局部操作的深度卷積神經(jīng)網(wǎng)絡(luò)車位占用檢測算法

doi: 10.11999/JEIT190349

申鉉京¹,
沈哲¹,
黃永平¹,
王玉^2, ,

1.
吉林大學(xué)計(jì)算機(jī)科學(xué)與技術(shù)學(xué)院長春 130012
2.
吉林大學(xué)應(yīng)用技術(shù)學(xué)院長春 130012

基金項(xiàng)目: 智慧法院智能化服務(wù)技術(shù)研究及支撐平臺(tái)開發(fā)(2018YFC0830100)，國家自然科學(xué)基金(61672259, 61876070)，國家自然科學(xué)基金青年科學(xué)基金(61602203)，吉林省科技發(fā)展計(jì)劃重點(diǎn)科技研發(fā)項(xiàng)目(20180201064SF)，吉林省優(yōu)秀青年人才基金(20180520020JH)

詳細(xì)信息

作者簡介:
申鉉京：男，1958年生，博士，教授，研究方向?yàn)閳D像處理與模式識(shí)別、多媒體信息安全、智能控制技術(shù)

沈哲：男，1995年生，碩士生，研究方向?yàn)閳D像處理與模式識(shí)別

黃永平：男，1964年生，博士，副教授，研究方向?yàn)閳D像處理與模式識(shí)別、智能控制與嵌入式系統(tǒng)

王玉：男，1983年生，博士，副教授，研究方向?yàn)閳D像處理與模式識(shí)別、多媒體信息技術(shù)

通訊作者:
王玉　wangyu001@jlu.edu.cn

中圖分類號(hào): TN911.73; TP391
計(jì)量
- 文章訪問數(shù): 2020
- HTML全文瀏覽量: 805
- PDF下載量: 101
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2019-05-17
- 修回日期: 2020-01-04
- 網(wǎng)絡(luò)出版日期: 2020-07-01
- 刊出日期: 2020-09-27

Deep Convolutional Neural Network for Parking Space Occupancy Detection Based on Non-local Operation

1.
College of Computer Science and Technology, Jilin University, Changchun 130012, China
2.
College of Applied Technology, Jilin University, Changchun 130012, China

Funds: The Intelligent Court Intelligent Service Technology Research and Support Platform Development (2018YFC0830100), The National Natural Science Foundation of China (61672259, 61876070), The National Natural Science Foundation of China Youth Science Foundation (61602203), The Key Scientific and Technological R & D Projects of Jilin Province Science and Technology Development Plan(20180201064SF), Jilin Province Outstanding Young Talent Fund Project (20180520020JH)

摘要

摘要: 隨著城市交通智能化發(fā)展，準(zhǔn)確高效地獲取可用車位對(duì)于解決日益嚴(yán)峻的停車難問題至關(guān)重要。該文提出一種基于非局部操作的深度卷積神經(jīng)網(wǎng)絡(luò)車位占用檢測算法。針對(duì)停車位圖像特性，引入非局部操作，度量遠(yuǎn)距離像素間的相似性，直接獲取邊緣高頻特征；使用小卷積核獲取局部細(xì)節(jié)特征；以端到端的方式訓(xùn)練網(wǎng)絡(luò)。實(shí)驗(yàn)中，通過設(shè)置不同卷積核尺寸和非局部模塊層數(shù)，優(yōu)化網(wǎng)絡(luò)結(jié)構(gòu)。實(shí)驗(yàn)結(jié)果表明，該文所提算法與傳統(tǒng)的基于紋理特征的車位占用檢測算法相比，無論在預(yù)測精度還是模型的泛化性能，均具有顯著的優(yōu)勢。與當(dāng)前廣泛應(yīng)用的基于局部特征提取的卷積神經(jīng)網(wǎng)絡(luò)相比，該算法具有較大的優(yōu)勢。在真實(shí)場景中，該算法同樣具有較高精度，具備實(shí)際應(yīng)用價(jià)值。
- 車位占用檢測 /
- 紋理特征 /
- 卷積神經(jīng)網(wǎng)絡(luò) /
- 非局部操作
Abstract: With the intelligent development of urban traffic, accurate and efficient access to available parking spaces is essential to solve the increasingly difficult problem of parking difficulties. Therefore, this paper proposes a deep convolutional neural network parking occupancy detection algorithm based on non-local operation. For the image characteristics of parking spaces, non-local operations are introduced, the similarity between distant pixels is measured, and the high-frequency features of the edges are directly obtained. The local details are obtained by using small convolution kernels, and the network is trained in an end-to-end manner. In the experiment, the network structure is optimized by setting different convolution kernel sizes and non-local module layers. The experimental results show that compared with the traditional texture feature-based parking space occupancy detection algorithm, the proposed algorithm has significant advantages in both prediction accuracy and generalization performance of the model. At the same time, compared with the currently widely used convolutional neural network based on local feature extraction, the algorithm also has great advantages. In real scenes, the algorithm also has high precision and has practical application value.
- Parking space occupancy detection /
- Texture feature /
- Convolutional neural network /
- Non-local operation

HTML全文

圖 1 非局部模塊

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圖 2 停車位圖像

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圖 3 模型結(jié)構(gòu)圖

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圖 4 不同卷積核尺寸的準(zhǔn)確率曲線圖

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圖 5 不同層數(shù)非局部模塊的準(zhǔn)確率曲線圖

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圖 6 可視化的特征圖

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圖 7 PKLot, CNRPark數(shù)據(jù)集間實(shí)驗(yàn)準(zhǔn)確率對(duì)比柱狀圖

下載: 全尺寸圖片幻燈片

圖 8 匡亞明樓停車場車位占用情況檢測結(jié)果

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表 1 不同卷積核尺寸的準(zhǔn)確率詳細(xì)對(duì)比(%)

卷積核尺寸	訓(xùn)練精度	測試精度
卷積核尺寸	訓(xùn)練精度	UFPR04	UFPR05	PUCPR
3	99.97	99.74	96.40	97.48
5	99.90	99.78	97.67	97.85
7	99.56	99.72	96.00	96.78
9	99.44	99.41	94.81	96.38
11	99.41	99.25	92.18	95.39

下載: 導(dǎo)出CSV

表 2 不同層數(shù)非局部模塊的準(zhǔn)確率詳細(xì)對(duì)比(%)

非局部模塊層數(shù)	訓(xùn)練精度	測試精度
非局部模塊層數(shù)	訓(xùn)練精度	UFPR04	UFPR05	PUCPR
1	99.90	99.78	97.67	97.85
2	99.96	99.81	97.65	97.55
3	99.95	99.85	98.55	98.35

下載: 導(dǎo)出CSV

表 3 不同方法的PKLot子數(shù)據(jù)集內(nèi)測試準(zhǔn)確率(%)

訓(xùn)練集	UFPR04	UFPR05	PUCPR
測試集	UFPR04	UFPR05	PUCPR
本文方法	99.85	99.62	99.92
mAlexnet	99.54	99.49	99.90
LPQu	99.50	98.90	99.58
Mean	99.64	99.30	99.61

下載: 導(dǎo)出CSV

表 4 不同方法的PKLot子數(shù)據(jù)集間測試準(zhǔn)確率(%)

訓(xùn)練集	測試集	方法	精度
UFPR04	UFPR05	本文方法	98.55
		mAlexnet^[14]	93.29
		LPQg^[18]	84.92
		Max	88.33
	PUCPR	本文方法	98.31
		mAlexnet^[14]	98.27
		LPQg^[18]	84.25
		Mean	88.40
UFPR05	UFPR04	本文方法	94.45
		mAlexnet^[14]	93.69
		LPQg^[18]	85.76
		Mean	85.53
	PUCPR	本文方法	95.87
		mAlexnet^[14]	92.72
		LPQu^[17]	87.74
		Mean	89.83
PUCPR	UFPR04	本文方法	99.24
		mAlexnet^[14]	98.03
		LPQg^[18]	87.15
		Mean	88.88
	UFPR05	本文方法	98.89
		mAlexnet^[14]	96.00
		LBPri^[19]	82.78
		Mean	84.20

下載: 導(dǎo)出CSV

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