基于紅外注意力提升機(jī)制的熱成像測(cè)溫區(qū)域?qū)嵗指?/h2>
doi: 10.11999/JEIT200862
cstr: 32379.14.JEIT200862
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1.
成都理工大學(xué)信息科學(xué)與技術(shù)學(xué)院(網(wǎng)絡(luò)安全學(xué)院、牛津布魯克斯學(xué)院) 成都 610059
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工業(yè)物聯(lián)網(wǎng)與網(wǎng)絡(luò)化控制教育部重點(diǎn)實(shí)驗(yàn)室 重慶 400065
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3.
太赫茲科學(xué)技術(shù)四川省重點(diǎn)實(shí)驗(yàn)室 成都 610054
基金項(xiàng)目: 國(guó)家自然科學(xué)基金(61771096)，工業(yè)物聯(lián)網(wǎng)與網(wǎng)絡(luò)化控制教育部重點(diǎn)實(shí)驗(yàn)室開放基金(2020FF06)，太赫茲科學(xué)技術(shù)四川省重點(diǎn)實(shí)驗(yàn)室開放基金(THZSC202001)
詳細(xì)信息
成都理工大學(xué)信息科學(xué)與技術(shù)學(xué)院(網(wǎng)絡(luò)安全學(xué)院、牛津布魯克斯學(xué)院) 成都 610059
工業(yè)物聯(lián)網(wǎng)與網(wǎng)絡(luò)化控制教育部重點(diǎn)實(shí)驗(yàn)室 重慶 400065
太赫茲科學(xué)技術(shù)四川省重點(diǎn)實(shí)驗(yàn)室 成都 610054
基金項(xiàng)目: 國(guó)家自然科學(xué)基金(61771096)，工業(yè)物聯(lián)網(wǎng)與網(wǎng)絡(luò)化控制教育部重點(diǎn)實(shí)驗(yàn)室開放基金(2020FF06)，太赫茲科學(xué)技術(shù)四川省重點(diǎn)實(shí)驗(yàn)室開放基金(THZSC202001)
Instance Segmentation of Thermal Imaging Temperature Measurement Region Based on Infrared Attention Enhancement Mechanism
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College of Information Science and Technology(College of Cyber Security, College of Oxford Brookes), Chengdu University of Technology, Chengdu 610059, China
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Key Laboratory of Industrial Internet of Things & Networked Control, Ministry of Education, Chongqing 400065, China
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Terahertz Science and Technology Key Laboratory of Sichuan Province, Chengdu 610054, China
Funds: The National Natural Science Foundation of China (61771096), The Key Laboratory of Industrial Internet of Things & Networked Control Foundation, Ministry of Education (2020FF06), The Terahertz Science and Technology Key Laboratory Foundation of Sichuan Province (THZSC202001)
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摘要: AI+熱成像人體溫度監(jiān)測(cè)系統(tǒng)被廣泛用于人群密集的人體實(shí)時(shí)溫度測(cè)量。此類系統(tǒng)檢測(cè)人的頭部區(qū)域進(jìn)行溫度測(cè)量，由于各類遮擋，溫度測(cè)量區(qū)域可能太小而無(wú)法正確測(cè)量。為了解決這個(gè)問題，該文提出一種融合紅外注意力提升機(jī)制的無(wú)錨點(diǎn)實(shí)例分割網(wǎng)絡(luò)，用于實(shí)時(shí)紅外熱成像溫度測(cè)量區(qū)域?qū)嵗指?。該文所提出的?shí)例分割網(wǎng)絡(luò)在檢測(cè)階段和分割階段融合紅外空間注意力模塊(ISAM)，旨在準(zhǔn)確分割紅外圖像中的頭部裸露區(qū)域，以進(jìn)行準(zhǔn)確實(shí)時(shí)的溫度測(cè)量。結(jié)合公共熱成像面部數(shù)據(jù)集和采集的紅外熱成像數(shù)據(jù)集，制作了“熱成像溫度測(cè)量區(qū)域分割數(shù)據(jù)集”用于網(wǎng)絡(luò)訓(xùn)練。實(shí)驗(yàn)結(jié)果表明：該方法對(duì)紅外熱成像圖像中頭部裸露測(cè)溫區(qū)域的平均檢測(cè)精度達(dá)到88.6%，平均分割精度達(dá)到86.5%，平均處理速度達(dá)到33.5 fps，在評(píng)價(jià)指標(biāo)上優(yōu)于大多數(shù)先進(jìn)的實(shí)例分割方法。Abstract: AI+thermal imaging human body temperature monitoring system is widely used for real-time temperature measurement of human body in dense crowds. The artificial intelligence method used in such systems detects the human head region for temperature measurement. The temperature measurement area may be too small to measure correctly due to occlusion. To tackle this problem, an anchor-free instance segmentation network incorporating infrared attention enhancement mechanism is proposed for real-time infrared thermal imaging temperature measurement area segmentation. The instance segmentation network proposed in this paper integrates the Infrared Spatial Attention Module (ISAM) in the detection stage and the segmentation stage, aiming to accurately segment the bare head area in the infrared image. Combined with the public thermal imaging facial dataset and the collected infrared thermal imaging dataset, the "thermal imaging temperature measurement area segmentation dataset" is produced. Experimental results demonstrate that this method reached an average detection precision of 88.6%, average mask precision of 86.5%, average processing speed of 33.5 fps. This network is superior to most state of the art instance segmentation methods in objective evaluation metrics.
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表 2 數(shù)據(jù)集中標(biāo)簽對(duì)應(yīng)的標(biāo)注色
分割類別 溫度測(cè)量區(qū)域 頭發(fā) 帽子 眼鏡 口罩 R 0 139 0 255 255 G 100 0 255 105 255 B 0 139 127 180 210 
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表 3 ISAM模塊目標(biāo)檢測(cè)階段有效性驗(yàn)證實(shí)驗(yàn)結(jié)果
結(jié)構(gòu) AP(%) AMP(%) fps 無(wú)注意力提升機(jī)制 83.7 80.0 38.0 融合CBAM模塊 85.6 81.5 33.5 融合BAM模塊 84.8 81.0 34.5 融合ISAM模塊 88.6 84.5 35.5
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表 4 ISAM模塊目標(biāo)分割階段有效性實(shí)驗(yàn)結(jié)果
結(jié)構(gòu) AP(%) AMP(%) fps 無(wú)注意力提升機(jī)制 88.6 84.5 35.5 融合CBAM模塊 88.6 84.3 31.2 融合BAM模塊 88.6 84.1 32.5 融合ISAM模塊 88.6 86.5 33.5
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表 5 熱成像測(cè)溫區(qū)域?qū)嵗指罘椒▽?duì)比實(shí)驗(yàn)結(jié)果
實(shí)例分割方法 AP(%) AMP(%) fps Mask R-CNN 83.8 80.3 11.5 YOLACT 75.6 70.1 38.0 CenterMask 82.8 78.1 33.0 PolarMask 83.6 79.0 30.5 本文方法 88.6 86.5 33.5
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