李小偉 王建業(yè)
摘要:隔爆外殼外的電纜和電氣設(shè)備漏電、大功率無線電發(fā)射在金屬支護(hù)和機(jī)電設(shè)備金屬上感生電動勢放電產(chǎn)生的礦井電火花,會引起瓦斯和煤塵爆炸及礦井火災(zāi)事故,因此有必要盡早感知礦井電火花。影響礦井電火花識別的主要是礦井光源,為減少礦井光源對礦井電火花圖像識別的干擾,提出了一種高采樣頻率的礦井電火花圖像識別及抗干擾方法:依據(jù)電火花的最長持續(xù)發(fā)光時(shí)間和閃光光源的最短持續(xù)發(fā)光時(shí)間,計(jì)算攝像機(jī)的采樣頻率,保證每次電火花出現(xiàn)時(shí),電火花圖像只出現(xiàn)在1幀圖像上,且礦井光源存在時(shí),干擾光源圖像至少出現(xiàn)在連續(xù)2幀圖像上;計(jì)算每幀圖像的像素灰度和,若當(dāng)前幀圖像的像素灰度和與前后相鄰幀圖像的像素灰度和的差值均大于設(shè)定的閾值,則發(fā)出礦井電火花報(bào)警信號。試驗(yàn)結(jié)果表明:在無干擾光源條件下,該方法可準(zhǔn)確識別礦井電火花圖像,準(zhǔn)確率達(dá)100%;在有日光燈、白熾燈等常亮光源干擾條件下,電火花與日光燈混合圖像中電火花識別準(zhǔn)確率達(dá)99.40%,電火花與白熾燈混合圖像中電火花識別準(zhǔn)確率達(dá)99.67%;在有閃光光源干擾條件下,電火花與閃光燈混合圖像中電火花識別準(zhǔn)確率達(dá)100%。
關(guān)鍵詞:礦井電火花;電火花識別;圖像識別;礦井光源;高采樣頻率
中圖分類號: TD67??? 文獻(xiàn)標(biāo)志碼: A
Research on high sampling frequency mine electric spark image recognition and anti-interference methods
LI Xiaowei, WANG Jianye
(School of Artificial Intelligence, China University of Mining and Technology-Beijing, Beijing 100083, China)
Abstract: Leakage of electricity from cables and electrical equipment outside the explosion-proof enclosure, and minesparks generated by high-power radio transmissions on metal supports and metal of electromechanical equipment due to induced electromotive discharges, can cause gas and coal dust explosions and mine fires. Therefore, it is necessary to detect mine electrical sparks as soon as possible. The main factor affecting the recognition of mine electric sparks is the mine light source. In order to reduce the interference of mine light sources on mine electric spark image recognition, a high sampling frequency mine electric spark image recognition and anti-interference method has been proposed. Based on the longest continuous emission time of the electric spark and the shortest continuous emission time of the flashlight source, the sampling frequency of the camera is calculated to ensure that the electric spark image only appears in one frame of the image each time the electric spark appears. When the mine light source exists, the interference light source image appears on at least 2 consecutive frames of image. The method calculates the pixel grayscale sum of each image frame. If the difference between the pixel grayscale of the current frame image and the pixel grayscale sum of adjacent frames is greater than the set threshold, a mine electric spark alarm signal will be issued. The experimental results show that under the condition of no interference light source, this method can accurately recognize mine electric spark images with an accuracy rate of 100%. Under the interference of constant light sources such as fluorescent lamps and incandescent lamps, the recognition accuracy of electric sparks in mixed images of electric sparks andfluorescent lamps reaches 99.40%. The recognition accuracy of electric sparks in mixed images of electric sparks and incandescent lamps reaches 99.67%. Under the interference of a flashing light source, the accuracy of electric spark recognition in the mixed image of electric spark and flash lamp reaches 100%.
Key words: mine electric spark; spark recognition; image recognition; mine light source; high sampling frequency
0 引言
煤礦井下電火花(以下簡稱礦井電火花)會引起瓦斯和煤塵爆炸及礦井火災(zāi)事故[1-4],嚴(yán)重威脅煤礦安全生產(chǎn)[4-6]。礦井電火花因其出現(xiàn)位置不同分為隔爆外殼內(nèi)電火花和隔爆外殼外電火花[2-3]。因隔爆外殼的隔爆作用,電氣開關(guān)分合和電氣設(shè)備故障等產(chǎn)生的隔爆外殼內(nèi)電火花,不會引起隔爆外殼外的瓦斯和煤塵爆炸及礦井火災(zāi)。而直接暴露在煤礦井下空間中的電纜和電氣設(shè)備漏電、大功率無線電發(fā)射在金屬支護(hù)和機(jī)電設(shè)備金屬上感生電動勢放電產(chǎn)生的電火花,會引起瓦斯和煤塵爆炸及礦井火災(zāi)事故[1-3]。據(jù)統(tǒng)計(jì)[5-6],2005—2014年全國煤礦發(fā)生一次死亡10人以上重特大瓦斯爆炸事故108起,其中電氣火源引爆瓦斯事故48起,事故占比高達(dá)44.4%;在引爆瓦斯的各種火源中,電氣火源引起瓦斯爆炸最多。因此,盡早感知礦井電火花,快速啟動應(yīng)急響應(yīng),能夠最大程度地保證人身及財(cái)產(chǎn)安全,具有重要意義[6-9]。
礦井電火花的產(chǎn)生具有偶發(fā)性,每次放電時(shí)間極短,加之其他光源干擾,很難被發(fā)現(xiàn)[3]。煤礦井下沒有日光、月光、星光和閃電等自然光源,影響礦井電火花識別的主要是 LED 燈[10]、白熾燈和日光燈等礦井光源。為減少礦井光源對礦井電火花圖像識別的影響,針對煤礦井下環(huán)境特點(diǎn),本文提出了一種高采樣頻率的礦井電火花圖像識別及抗干擾方法。
1 高采樣頻率的礦井電火花圖像識別及抗干擾方法
礦井光源可分為固定光源(如巷道燈、巷道指示燈等)和移動光源[11](如礦燈、車燈等),又可分為常亮光源(如巷道燈、礦燈、車燈等)和閃光光源(如閃爍的信號燈、指示燈等)。研究表明,常亮移動光源和閃光移動光源對固定攝像機(jī)的照射都是非連續(xù)的[3]。因此,可以將常亮移動光源和閃光移動光源對固定攝像機(jī)的干擾統(tǒng)一按閃光光源處理。
高采樣頻率的礦井電火花圖像識別及抗干擾方法原理:將電火花的最長持續(xù)發(fā)光時(shí)間[12-14]和閃光光源的最短持續(xù)發(fā)光時(shí)間[15-18]作為限制條件,計(jì)算攝像機(jī)的采樣頻率;保證每次電火花出現(xiàn)時(shí),電火花圖像只出現(xiàn)在1幀圖像上,且礦井光源存在時(shí),干擾光源圖像至少出現(xiàn)在連續(xù)2幀圖像上;計(jì)算每幀圖像的像素灰度和,若當(dāng)前幀圖像的像素灰度和與前后相鄰幀圖像的像素灰度和的差值均大于設(shè)定的閾值,則發(fā)出礦井電火花報(bào)警信號。
1.1 攝像機(jī)采樣頻率
為保證電火花圖像只出現(xiàn)在1幀圖像上,且干擾光源圖像至少出現(xiàn)在連續(xù)2幀圖像上,攝像機(jī)采樣頻率 F 應(yīng)滿足以下要求:
式中:GTmin為閃光光源的最短持續(xù)發(fā)光時(shí)間;DTmax為電火花的最長持續(xù)發(fā)光時(shí)間。
研究表明[3,14,17],閃光光源的最短持續(xù)發(fā)光時(shí)間GTmin=240 ms,電火花的最長持續(xù)發(fā)光時(shí)間DTmax=4 ms,將其代入式(1),可得攝像機(jī)采樣頻率范圍:8.33 Hz<F<250 Hz。
當(dāng)攝像機(jī)采樣頻率為8.33~250 Hz 時(shí),能夠保證電火花圖像出現(xiàn)在1幀圖像上且不連幀,干擾光源圖像至少出現(xiàn)在連續(xù)2幀圖像上。為便于計(jì)算,本文取 F=200 Hz。
1.2 方法流程
高采樣頻率的礦井電火花圖像識別及抗干擾方法流程如圖1所示,具體步驟如下。
1)根據(jù)式(1)計(jì)算攝像機(jī)的采樣頻率范圍,并設(shè)定采樣頻率 F。
2)基于采樣頻率 F,對攝像機(jī)采集到的視頻圖像進(jìn)行分幀預(yù)處理,并分別計(jì)算單幀圖像的像素灰度和。
3)計(jì)算第i幀圖像的像素灰度和與第 i?1幀圖像的像素灰度和的差值:
式中:Hxy為像素點(diǎn)(x,y)像素值;M,Q 分別為像素點(diǎn)橫縱坐標(biāo)最大值。
4)判斷第i幀圖像的像素灰度和與第 i?1幀圖像的像素灰度和的差值 Ni?1是否大于設(shè)定的閾值 N。若是,則計(jì)算第i幀圖像的像素灰度和與第 i+1幀圖像的像素灰度和的差值Ni+1,否則返回繼續(xù)監(jiān)測。
5)判斷第i幀圖像的像素灰度和與第 i+1幀圖像的像素灰度和的差值 Ni+1是否大于設(shè)定的閾值 N。若是,則進(jìn)行礦井電火花報(bào)警,并啟動人工應(yīng)急響應(yīng),否則返回繼續(xù)監(jiān)測。若人工沒有啟動應(yīng)急響應(yīng),則繼續(xù)礦井電火花報(bào)警;若人工啟動應(yīng)急響應(yīng),則退出報(bào)警,返回繼續(xù)監(jiān)測。
2 高采樣頻率的礦井電火花圖像識別及抗干擾方法試驗(yàn)驗(yàn)證
本試驗(yàn)采用的電火花發(fā)生裝置及其產(chǎn)生的電火花如圖2所示。采用pco.ultraviolet型紫外攝像機(jī)(圖3(a))采集電火花圖像,攝像機(jī)分辨率為1042×1394,工作波段為190~1100 nm,攝像機(jī)曝光量和焦距手動可調(diào)。在攝像機(jī)前設(shè)置(365±10)nm 紫外濾光片(圖3(b)),濾光后成像波段為355~375 nm。攝像機(jī)采用獨(dú)立電源供電,并通過 USB 端口與計(jì)算機(jī)相連。
2.1 無光源干擾條件下礦井電火花圖像識別試驗(yàn)
無光源干擾條件下礦井電火花圖像識別試驗(yàn)過程:采集電火花圖像,經(jīng)分幀處理后得到600幀圖像樣本,其中包含28幀電火花圖像。采用本文方法進(jìn)行計(jì)算機(jī)識別,運(yùn)行程序得到的結(jié)果如圖4和圖5所示。由圖4可知,在無光源干擾條件下,有電火花的幀圖像像素灰度和與其相鄰幀無電火花背景圖像像素灰度和區(qū)分明顯。由圖5可知,遍歷所有幀圖像樣本,共檢測出有電火花的圖像28幀,其中誤檢0幀,漏檢0幀,能準(zhǔn)確識別電火花圖像。
2.2 有光源干擾條件下礦井電火花圖像識別試驗(yàn)
在井下實(shí)際環(huán)境中,存在著干擾電火花圖像識別的礦井光源。因此,本文進(jìn)行了有光源干擾(日光燈、白熾燈、閃光燈)條件下礦井電火花圖像識別試驗(yàn)。
1)使用紫外攝像機(jī)拍攝電火花與日光燈混合圖像,經(jīng)分幀處理后得到500幀圖像樣本,其中包含28幀電火花圖像,采用本文方法進(jìn)行計(jì)算機(jī)識別,運(yùn)行程序得到的結(jié)果如圖6和圖7所示。由圖6可知,雖然背景圖像整體像素灰度和在1.705×107~1.709× 107范圍內(nèi)波動,但本文方法根據(jù)前后相鄰幀圖像像素灰度和的差值判定電火花,存在電火花的幀圖像像素灰度和與其相鄰幀無電火花背景圖像像素灰度和區(qū)分依舊明顯。由圖7可知,遍歷所有幀圖像樣本,共檢測出電火花圖像29幀,其中第121幀、第325幀為非電火花圖像,但被識別為電火花圖像,誤檢2幀,第327幀為電火花圖像,但未被識別出來,漏檢1幀。
2)使用紫外攝像機(jī)拍攝電火花與白熾燈混合圖像,經(jīng)分幀處理后得到898幀圖像樣本,其中包含56幀電火花圖像。采用本文方法進(jìn)行計(jì)算機(jī)識別,運(yùn)行程序得到的結(jié)果如圖8和圖9所示。由圖8可知,雖然背景圖像整體像素灰度和在1.21×106~1.27×106范圍內(nèi)波動,但本文方法根據(jù)前后相鄰幀圖像像素灰度和的差值判定電火花,存在電火花的幀圖像像素灰度和與其相鄰幀無電火花背景圖像像素灰度和區(qū)分依舊明顯。由圖9可知,遍歷所有幀圖像樣本,共檢測出電火花圖像59幀,其中第121幀、第123幀、第169幀為非電火花圖像,但被識別為電火花圖像,誤檢3幀,漏檢0幀。
3)使用紫外攝像機(jī)拍攝電火花與閃光燈混合圖像,經(jīng)分幀處理后得到800幀圖像樣本,其中包含48幀電火花圖像。采用本文方法進(jìn)行計(jì)算機(jī)識別,運(yùn)行程序得到的結(jié)果如圖10和圖11所示。
由圖10可知,本文方法根據(jù)前后相鄰幀圖像像素灰度和的差值判定電火花,存在電火花的幀圖像像素灰度和與其相鄰幀無電火花背景圖像像素灰度和區(qū)分明顯。由圖11可知,遍歷所有幀圖像樣本,共檢測出有電火花的圖像48幀,其中誤檢0幀,漏檢0幀。
2.3 試驗(yàn)結(jié)果分析
采用精確率、準(zhǔn)確率及召回率[19-21]對試驗(yàn)結(jié)果進(jìn)行分析,見表1。
從表1可看出:在無光源干擾條件下,可準(zhǔn)確識別礦井電火花圖像,準(zhǔn)確率達(dá)100%;在常亮光源干擾條件下,電火花與日光燈混合圖像中電火花識別準(zhǔn)確率達(dá)99.40%;電火花與白熾燈混合圖像中電火花識別準(zhǔn)確率達(dá)99.67%;在閃光光源干擾條件下,電火花與閃光燈混合圖像中電火花識別準(zhǔn)確率達(dá)100%。
3 結(jié)論
1)提出了高采樣頻率的礦井電火花圖像識別及抗干擾方法:依據(jù)電火花的最長持續(xù)發(fā)光時(shí)間和閃光光源的最短持續(xù)發(fā)光時(shí)間,計(jì)算攝像機(jī)的采樣頻率,保證每次電火花出現(xiàn)時(shí),電火花圖像只出現(xiàn)在1幀圖像上,且礦井光源存在時(shí),干擾光源圖像至少出現(xiàn)在連續(xù)2幀圖像上;計(jì)算每幀圖像的像素灰度和,若當(dāng)前幀圖像像素灰度和與前后相鄰幀圖像像素灰度和的差值均大于設(shè)定的閾值,則發(fā)出礦井電火花報(bào)警信號。
2)試驗(yàn)結(jié)果表明:在無光源干擾條件下,該方法可準(zhǔn)確識別礦井電火花圖像,準(zhǔn)確率達(dá)100%;在有日光燈、白熾燈等常亮光源干擾條件下,電火花與日光燈混合圖像中電火花識別準(zhǔn)確率達(dá)99.40%,電火花與白熾燈混合圖像中電火花識別準(zhǔn)確率達(dá)99.67%。在有閃光光源干擾條件下,電火花與閃光燈混合圖像中電火花識別準(zhǔn)確率達(dá)100%。
參考文獻(xiàn)(References):
[1] 孫繼平.屯蘭煤礦“2·22”特別重大瓦斯爆炸事故原因及教訓(xùn)[J].煤炭學(xué)報(bào),2010,35(1):72-75.
SUN Jiping. The causes and lessons of "2.22" gas explosion disaster at Tunlan Coal Mine[J]. Journal of China Coal Society,2010,35(1):72-75.
[2] 孫繼平,李小偉,徐旭,等.礦井電火花及熱動力災(zāi)害紫外圖像感知方法研究[J].工礦自動化,2022,48(4):1-4,95.
SUN Jiping,LI Xiaowei,XU Xu,et al. Research on ultraviolet image perception method of mine electric spark and thermal power disaster[J]. Journal of Mine Automation,2022,48(4):1-4,95.
[3] 孫繼平,李小偉,王建業(yè).基于圖像鄰幀像素灰度和的礦井電火花識別及報(bào)警方法研究[J].工礦自動化,2023,49(7):1-5.
SUN Jiping,LI Xiaowei,WANG Jianye. Research on mine electric spark recognition and alarm method based on the sum of adjacent frame pixel grayscale of images[J]. Journal of Mine Automation,2023,49(7):1-5.
[4] 孫繼平.煤礦瓦斯和煤塵爆炸感知報(bào)警與爆源判定方法研究[J].工礦自動化,2020,46(6):1-5,11.
SUN Jiping. Research on method of coal mine gas and coal dust explosion perception alarm and explosion source judgment[J]. Industry and Mine Automation,2020,46(6):1-5,11.
[5] 孫繼平,錢曉紅.2004—2015年全國煤礦事故分析[J].工礦自動化,2016,42(11):1-5.
SUN Jiping,QIAN Xiaohong. Analysis of coal mine accidents in China during 2004-2015[J]. Industry and Mine Automation,2016,42(11):1-5.
[6] 孫繼平.互聯(lián)網(wǎng)+煤礦監(jiān)控與通信[M].北京:煤炭工業(yè)出版社,2016.
SUN Jiping. Internet+coal mine monitoring and communication[M]. Beijing: China Coal Industry Press,2016.
[7] 孫繼平.煤礦事故分析與煤礦大數(shù)據(jù)和物聯(lián)網(wǎng)[J].工礦自動化,2015,41(3):1-5.
SUN Jiping. Accident analysis and big data and Internet of things in coal mine[J]. Industry and Mine Automation,2015,41(3):1-5.
[8] 余星辰,李小偉.基于特征融合的煤礦瓦斯和煤塵爆炸聲音識別方法[J/OL].煤炭學(xué)報(bào):1-10[2023-07-28]. https://doi.org/10.13225/j.cnki.jccs.2022.1421.
YU Xingchen,LI Xiaowei. Sound recognition method of coal mine gas and coal dust explosion based on feature fusion[J/OL]. Journal of China Coal Society:1-10[2023-07-28]. https://doi.org/10.13225/j.cnki.jccs.2022.1421.
[9] 孫繼平,范偉強(qiáng).基于視頻圖像的瓦斯和煤塵爆炸感知報(bào)警及爆源判定方法[J].工礦自動化,2020,46(7):1-4,48.
SUN Jiping, FAN Weiqiang. Gas and coal dust explosion perception alarm and explosion source judgment method based on video image[J]. Industry and Mine Automation,2020,46(7):1-4,48.
[10] 徐曉冰,許可義,穆道明,等.礦井LED燈的發(fā)熱分析及光源設(shè)計(jì)[J].煤礦機(jī)械,2017,38(4):12-15.
XU Xiaobing,XU Keyi,MU Daoming,et al. Heat analysis and light source design of mine LED lamp[J]. Coal Mine Machinery,2017,38(4):12-15.
[11] 國家安全生產(chǎn)監(jiān)督管理總局.煤礦安全規(guī)程[M].北京:煤炭工業(yè)出版社,2022:2-115.
State Administration of Work Safety. Coal mine safety regulations[M]. Beijing:China Coal Industry Publishing House,2022:2-115.
[12] 陳坤,張小良,陶光遠(yuǎn),等.影響靜電火花放電的因素[J].中國粉體技術(shù),2021,27(5):1-10.
CHEN Kun,ZHANG Xiaoliang, TAO Guangyuan, et al. Influence factors of electrostatic? spark discharge[J]. China Powder Science and Technology,2021,27(5):1-10.
[13] 劉佳.靜電火花放電特性探究[D].大連:大連理工大學(xué),2020.
LIU Jia. Exploring the characteristics of electrostatic spark discharge[D]. Dalian: Dalian University of Technology,2020.
[14] 梁天宇.淺談電火花加工的要素[J].中國高新技術(shù)企業(yè),2015(4):91-92.
LIANG Tianyu. Discussion on the elements of electrical discharge machining[J]. China High-Tech Enterprises,2015(4):91-92.
[15] GB 17509—2008汽車及掛車轉(zhuǎn)向信號燈配光性能[S]. GB 17509-2008 Photometric characteristics of direction indicators for motor vehicles and their trailers[S].
[16] GB 14886—2016道路交通信號燈設(shè)置與安裝規(guī)范[S]. GB 14886-2016 Specifications for road traffic signal setting and installation[S].
[17] GA/T 743—2016閃光警告信號燈[S].
GA/T 743-2016 Flash alarm signals[S].
[18] JB/T 12707—2016道路監(jiān)控電子閃光裝置[S].
JB/T 12707-2016 Electronic flash apparatus for road monitoring[S].
[19] 曹玉超,范偉強(qiáng).基于不同深度識別算法的礦井水位標(biāo)尺刻度識別性能分析與研究[J].煤炭學(xué)報(bào),2019,44(11):3529-3538.
CAO Yuchao,F(xiàn)AN Weiqiang. Performance analysis and research of mine water level scale recognition based on different depth recognition algorithms[J]. Journal of China Coal Society,2019,44(11):3529-3538.
[20] 余星辰,王云泉.基于小波包能量的煤礦瓦斯和煤塵爆炸聲音識別方法[J].工礦自動化,2023,49(1):131-139.
YU Xingchen,WANG Yunquan. Coal mine gas and coal dust explosion sound recognition method based on wavelet packet energy[J]. Journal of Mine Automation,2023,49(1):131-139.
[21] 王建業(yè).礦井電火花圖像感知方法研究[D].北京:中國礦業(yè)大學(xué)(北京),2023:11-15.
WANG Jianye. Research on mine electric spark image perception method[D]. Beijing:China University of Mining and Technology-Beijing,2023:11-15.