[1]王晓林,苏松志,刘晓颖,等.一种基于级联神经网络的飞机检测方法[J].智能系统学报,2020,15(4):697-704.[doi:10.11992/tis.201908028]
WANG Xiaolin,SU Songzhi,LIU Xiaoying,et al.Cascade convolutional neural networks for airplane detection[J].CAAI Transactions on Intelligent Systems,2020,15(4):697-704.[doi:10.11992/tis.201908028]
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WANG Xiaolin,SU Songzhi,LIU Xiaoying,et al.Cascade convolutional neural networks for airplane detection[J].CAAI Transactions on Intelligent Systems,2020,15(4):697-704.[doi:10.11992/tis.201908028]
一种基于级联神经网络的飞机检测方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
15
期数:
2020年第4期
页码:
697-704
栏目:
学术论文—机器学习
出版日期:
2020-07-05
- Title:
- Cascade convolutional neural networks for airplane detection
- Keywords:
- airplane detection; remote sensing images; cascade; deep learning; convolutional neural network; two-stage; coarse-to-fine; embedded device
- 分类号:
- TP391.4
- 摘要:
- 由于旋转角度多样性、极端的尺度差异的影响，遥感图像中的飞机检测目前仍存在挑战。为了解决旋转和尺度的问题，目前的策略是将现有的自然场景下的目标检测算法(如Faster R-CNN、SSD等)直接迁移到遥感图像中。这些算法的主干网络复杂，模型占用空间大，难以应用到低功耗和嵌入式设备中。为了在准确率不降低的情况下提高检测速度，本文提出了一个仅包含9层的卷积神经网络来解决飞机检测问题。该网络采用了由粗到细的策略，通过级联两个网络的方式减少计算开销。为了评估方法的有效性，我们建立了一个针对飞机检测的遥感数据集。实验结果表明，该方法超越了VGG16等复杂的主干网络，达到了接近主流检测方法的性能表现，同时显著降低了参数量并使检测速度提高了2倍以上。
- Abstract:
- Detecting airplanes from remote sensing images remains a challenging task, since the images of airplanes always have the characteristics of multiple rotation angles and severe scale change. In order to solve these problems, the most commonly used strategies are to transfer the existing mainstream object detection algorithms based on natural scenario into the remote sensing images directly, such as Faster R-CNN or SSD. However, the backbones of such networks are generally heavy and occupying large space, which are difficult to be applied to low-power consumption devices or front-end embedded systems. To this end, we designed a simple convolutional neural network architecture with only 9 convolutional layers for airplane detection. Our method adopted a coarse-to-fine strategy by cascading a two-stage network, which further reducing the computation cost of detection. Finally, we built a remote sensing dataset for airplane detection to verify our proposed method. The experimental results show that compared with heavy backbone networks such as VGG16, the performance of our method is close to popular methods, but with much less parameters and more than 2 times higher detection speed.
- 参考文献/References:
-
[1] REN S, HE K, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks [C]//Proceedings of the 28th International Conference on Neural Information Processing System. Montreal, Canada, 2015: 91-99.
[2] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection[C]//Proceedi ngs of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA, 2015: 779-788.
[3] REDMON J, FARHADI A. YOLO9000: better, faster, strong-er[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA. 2017: 6517-6525.
[4] REDMON J, FARHADI A. YOLOv3: an incremental im-provement[J]. arXiv preprint arXiv: 1804.02767, 2018.
[5] LIU W, ANGUELOY D, ERHAN D, et al. SSD: single shot multibox detector[C]//Proceedings of the 14th European Conference on Computer Vision. Amsterdam, The Netherlands, 2015: 21-37.
[6] LIN T Y, GOYAL P, GIRSHICK R, et al. Focal loss for dense object detection[C]//Proceedings of the IEEE International Conference on Computer Vision. Venice, Italy, 2017: 2999-3007.
[7] LIN T Y, MAIRE M, MAIRE M, et al. Microsoft COCO: common objects in context[C]// Proceedings of the 14th European Conference on Computer Vision. Zurich, Switzerland, 2014: 740-755.
[8] EVERINGHAM M, GOOL L J V. The PASCAL visual object classes challenge[J]. International journal of computer vision, 2010, 88(2): 303-338.
[9] DALAL N, TRIGGS B. Histograms of oriented gradients for human detection[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. San Diego, USA, 2005: 886-893.
[10] LOWE D G. Distinctive image features from scale-invariant keypoints[J]. International journal of computer vision, 2004, 60(2): 91-110.
[11] CORTES C, VAPNIK V. Support-vector networks[J]. Machine learning, 1995, 20(3): 273-297.
[12] UIJLINGS J, SANDE K, GEVERS T, et al. Selective search for object recognition[J]. International journal of computer vision, 2013, 104(2): 154-171.
[13] GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Columbus, USA, 2014: 580-587.
[14] GIRSHICK R. Fast R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision. Santiago, Chile, 2015: 1440-1448.
[15] AN Z, SHI Z, TENG X, et al. An automated airplane detection system for large panchromatic image with high spatial resolution[J]. Optik, 2014, 125(12): 2768-2775.
[16] LI W, XIANG S, WANG H, et al. Robust airplane detection in satellite images[C]//Proceedings of International Conference on Image Processing. Brussels, Belgium, 2011: 2821-2824.
[17] HSIEH M R, LIN Y L, HSU W H. Drone-based object counting by spatially regularized regional proposal[C]// Proceedings of the IEEE International Conference on Computer Vision. Venice, Italy, 2017: 4165-4173.
[18] LIU L, PAN Z, LEI B. Learning a rotation invariant detector with rotatable bounding box[J]. arXiv preprint arXiv: 1711.09405, 2017.
[19] YANG Y, ZHUANG Y, BI F, et al. M-FCN: effective fully convolutional network-based airplane detection Frame-work[J]. IEEE geoscience and remoting sensing letters, 2017, 14(8): 1293-1297.
[20] CHENG G, HAN J, ZHOU P, et al. Multi-class geospatial object detection and geographic image classification based on collection of part detectors[J]. ISPRS journal of photogrammetry and remote sensing, 2014, 98(1): 119-132.
[21] CHENG G, HAN J. A Survey on object detection in optical remote sensing images[J]. ISPRS Journal of photogrammetry and remote sensing, 2016, 117: 11-28.
[22] CHENG G, ZHOU P, HAN J. Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images[J]. IEEE transactions on geoscience and remote sensing, 2016, 54(12): 7405-7415.
[23] XIA G S, BAI X, DING J, et al. DOTA: A large-scale dataset for object detection in aerial images[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA, 2018: 3974-3983.
[24] ZHANG K, ZHANG Z, LI Z, et al. Joint face detection and alignment using multitask cascaded convolutional net-works[J]. IEEE signal processing letters, 2016, 23(10): 1499-1503.
[25] HE K, GKIOXARI G, DOLLáR P, et al. Mask R-CNN[C] //Proceedings of the IEEE International Conference on Computer Vision. Venice, Italy, 2017: 2980-2988.
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备注/Memo
收稿日期:2019-08-24。
基金项目:国家自然科学基金项目（61806172，41971424）；厦门市海洋与渔业局海洋科技成果转化与产业化示范项目（18CZB033HJ11）
作者简介:王晓林，硕士研究生，主要研究方向为目标检测;苏松志，副教授，主要研究方向为计算机视觉、机器学习、人脸识别与行人检测。发表学术论文30余篇;李绍滋，教授，博士生导师，主要研究方向为计算机视觉、机器学习和数据挖掘。先后主持或参加多项国家863项目、国家自然科学基金项目、教育部博士点基金项目、省科技重点项目等多个项目的研究。发表学术论文300余篇
通讯作者:苏松志.E-mail:ssz@xmu.edu.cn
更新日期/Last Update:
2020-07-25