針對遙感影像的MSA-YOLO儲油罐目標(biāo)檢測
2022年電子技術(shù)應(yīng)用第11期
李 想1,,2,,特日根1,2,,趙宇恒1,2,,陳文韜1,,2,徐國成3
1.長光衛(wèi)星技術(shù)股份有限公司,,吉林 長春130000,; 2.吉林省衛(wèi)星遙感應(yīng)用技術(shù)重點(diǎn)實(shí)驗(yàn)室,吉林 長春130000,; 3.吉林大學(xué) 材料科學(xué)與工程學(xué)院,,吉林 長春130000
摘要: 原油作為一種重要的戰(zhàn)略物資,在我國經(jīng)濟(jì)和軍事等多個(gè)領(lǐng)域均起到重要作用,。提出一種算法MSA-YOLO(MultiScale Adaptive YOLO),,該算法在YOLOv4算法的基礎(chǔ)上進(jìn)行優(yōu)化,并基于以吉林一號光學(xué)遙感衛(wèi)星影像為主的遙感圖像數(shù)據(jù)集進(jìn)行實(shí)驗(yàn),,對特定監(jiān)控區(qū)域內(nèi)的儲油罐進(jìn)行識別與分類,。算法優(yōu)化內(nèi)容包括:為簡化儲油罐監(jiān)測模型同時(shí)保證模型的效率,對YOLOv4的網(wǎng)絡(luò)結(jié)構(gòu)中的多尺度識別模塊進(jìn)行修剪,;使用k-means++聚類算法進(jìn)行初始錨框的選取,,使模型加速收斂;使用基于CIoU-NMS的優(yōu)化,,進(jìn)一步提升推理速度和準(zhǔn)確度,。實(shí)驗(yàn)結(jié)果表明,與YOLOv4相比,,MSA-YOLO模型參數(shù)數(shù)量減少25.84%,;模型尺寸減少62.13%;在Tesla V100的GPU環(huán)境下,,模型的訓(xùn)練速度提升6 s/epoch,,推理速度提升15.76 F/s;平均精度為95.65%,。與此同時(shí),,MSA-YOLO算法在多種通用目標(biāo)識別算法進(jìn)行的對比實(shí)驗(yàn)中均體現(xiàn)出了更高效的特點(diǎn)。MSA-YOLO算法對儲油罐進(jìn)行準(zhǔn)確且實(shí)時(shí)的識別具有通用可行性,,可為遙感數(shù)據(jù)在能源期貨領(lǐng)域提供技術(shù)參考,。
中圖分類號: TP75
文獻(xiàn)標(biāo)識碼: A
DOI:10.16157/j.issn.0258-7998.223191
中文引用格式: 李想,特日根,,趙宇恒,,等. 針對遙感影像的MSA-YOLO儲油罐目標(biāo)檢測[J].電子技術(shù)應(yīng)用,2022,,48(11):24-32,,40
英文引用格式: Li Xiang,,Te Rigen,Zhao Yuheng,,et al. MSA-YOLO oil storage tank target detection for remote sensing images[J]. Application of Electronic Technique,,2022,,48(11):24-32,,40
文獻(xiàn)標(biāo)識碼: A
DOI:10.16157/j.issn.0258-7998.223191
中文引用格式: 李想,特日根,,趙宇恒,,等. 針對遙感影像的MSA-YOLO儲油罐目標(biāo)檢測[J].電子技術(shù)應(yīng)用,2022,,48(11):24-32,,40
英文引用格式: Li Xiang,,Te Rigen,Zhao Yuheng,,et al. MSA-YOLO oil storage tank target detection for remote sensing images[J]. Application of Electronic Technique,,2022,,48(11):24-32,,40
MSA-YOLO oil storage tank target detection for remote sensing images
Li Xiang1,2,,Te Rigen1,,2,Zhao Yuheng1,,2,,Chen Wentao1,2,,Xu Guocheng3
1.Chang Guang Satellite Technology Co.,,Ltd.,Changchun 130000,,China,; 2.Main Laboratory of Satellite Remote Sensing Technology of Jilin Province,Changchun 130000,,China,; 3.School of Materials Science and Engineering,Jilin University,,Changchun 130000,,China
Abstract: Crude oil, as an important strategic material, plays an important role in many fields such as my country′s economy and military. This paper proposes an algorithm MSA-YOLO(MultiScale Adaptive YOLO), which is optimized on the basis of the YOLOv4 algorithm, and is experimented based on the remote sensing image dataset mainly based on Jilin-1 optical remote sensing satellite images,to make identification and classification of oil storage tanks. The algorithm optimization contents include: in order to simplify the oil storage tank monitoring model and ensure the efficiency of the model, prune the multi-scale identification module in the network structure of YOLOv4; use the k-means++ clustering algorithm to select the initial anchor frame to accelerate the convergence of the model;use CIoU-NMS-based optimization to further improve inference speed and accuracy. The experimental results show that compared with YOLOv4, the number of parameters of MSA-YOLO model is reduced by 25.84%; the model size is reduced by 62.13%; in the GPU environment of Tesla V100, the training speed of the model is increased by 6 s/epoch, and the inference speed is increased by 15.76 F/s; the average accuracy is 95.65%. At the same time, the MSA-YOLO algorithm shows more efficient characteristics in the comparative experiments of various general target recognition algorithms. The MSA-YOLO algorithm has universal feasibility for accurate and real-time identification of oil storage tanks, and can provide technical reference for remote sensing data in the field of energy futures.
Key words : computer vision,;target recognition,;deep learning;YOLO,;sorage tank detection
0 引言
近年來,,隨著高分辨率光學(xué)衛(wèi)星遙感影像處理技術(shù)的快速發(fā)展,基于遙感影像的目標(biāo)識別取得了大量成果,。其中,,對地表自然形成或人造物體進(jìn)行識別一直是從業(yè)人員的關(guān)注重點(diǎn)之一。儲油罐是在石油,、天然氣等石化行業(yè)中使用的設(shè)備,,用于儲存在環(huán)境溫度下為液態(tài)的原油或者其他化工產(chǎn)品度下為液態(tài)的原油或者其他化工產(chǎn)品。按照儲油罐的不同用途,,分為固定頂型和外浮頂型,。利用遙感影像的太陽高度角和內(nèi)外陰影參數(shù),,可以對外浮頂儲油罐的滿油率進(jìn)行估算,通過滿油率數(shù)據(jù)在能源期貨價(jià)格的預(yù)測模型中進(jìn)行回歸分析,,不但可以為能源期貨交易機(jī)構(gòu)提供參考,,還能對我國原油的采購及存儲等起到指導(dǎo)作用。而在上述工作中,,首要任務(wù)是在高分辨率遙感影像中實(shí)現(xiàn)固定頂和外浮頂儲油罐的高效識別與分類,。
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作者信息:
李 想1,,2,,特日根1,2,,趙宇恒1,,2,陳文韜1,,2,,徐國成3
(1.長光衛(wèi)星技術(shù)股份有限公司,吉林 長春130000,;
2.吉林省衛(wèi)星遙感應(yīng)用技術(shù)重點(diǎn)實(shí)驗(yàn)室,,吉林 長春130000;
3.吉林大學(xué) 材料科學(xué)與工程學(xué)院,,吉林 長春130000)
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