Abstract
Object detection in remote sensing images is full of challenges. The large scale of detection region, and the difficulty of dense object detection are the main problems needed to solve. Although YOLOv3 perform better and faster, it is not suitable to the orientation which means it is not friendly to NMS operation on dense objects area. This drawback would lead to an increase in miss rate in dense object detection. In this paper, we modified YOLOv3 based on the oriented bounding box (OBB) for object detection in remote images to solve the problems above. This model is based on YOLOv3 which performs better on small target compared with YOLOv2. We modified the architecture of YOLOv3 to predict oriented bounding box. In this way, we can obtain bounding boxes more suitable for large aspect ratio objects. Moreover, in a dense area such as parking lot, it can also achieve a good performance.
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References
Cheng, G., Han, J.: A survey on object detection in optical remote sensing images. Isprs J. Photogr. Remote Sens. 117, 11–28 (2016)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Region-based convolutional networks for accurate object detection and segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 38(1), 142–158 (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–16 (2014)
Girshick, R.: FAST R-CNN. In: IEEE International Conference on Computer Vision (2015)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: International Conference on Neural Information Processing Systems (2015)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: IEEE Conference on Computer Vision and Pattern Recognition (2017)
Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. arXiv (2018)
Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Shelhamer, E., Long, J., Darrell, T.: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell. PP(99), 1–1 (2014)
Dai, J., Li, Y., He, K., Sun, J.: R-FCN: Object detection via region-based fully convolutional networks. In: Neural Information Processing Systems (2016)
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan,B., Belongie, S.: Feature pyramid networks for object detection. In IEEE Conference on Computer Vision and Pattern Recognition (2017)
Lin, H., Shi, Z., Zou, Z.: Fully convolutional network with task partitioning for inshore ship detection in optical remote sensing images. IEEE Geosci. Remote Sens. Lett. PP(99), 1–5 (2017)
Kang, M., Leng, X., Lin, Z., Ji, K.: A modified faster R-CNN based on CFAR algorithm for SAR ship detection. In: International Workshop on Remote Sensing with Intelligent Processing (2017)
Zhang, R., Yao, J., Zhang, K., Feng, C., Zhang, J.: S-CNN-based ship detection from high-resolution remote sensing images. Int. Arch. Photogr. Sens. Spat. Inform. Sci. XLI–B7, 423–430 (2016)
Kang, M., Ji, K., Leng, X., Lin, Z.: Contextual region-based convolutional neural network with multilayer fusion for SAR ship detection. Remote Sens. 9(8), 860 (2017)
Van Etten, A.: You only look twice: rapid multi-scale object detection in satellite imagery. arXiv (2018)
Ma, J.: Arbitrary-oriented scene text detection via rotation proposals. IEEE Trans. Multimed. PP(99), 1 (2017)
Jiang, Y.: R2CNN: rotational region CNN for orientation robust scene text detection. arXiv (2017)
Shi, B., Bai, X., Belongie, S.: Detecting oriented text in natural images by linking segments. In: IEEE Conference on Computer Vision and Pattern Recognition (2017)
Xia, G.-S.: DOTA: a large-scale dataset for object detection in aerial images. In: The IEEE Conference on Computer Vision and Pattern Recognition (2018)
Yang, X., Sun, H., Sun, X., Yan, M., Guo, Z., Kun, F.: Position detection and direction prediction for arbitrary-oriented ships via multitask rotation region convolutional neural network. IEEE Access 6, 50839–50849 (2018)
Bodla, N., Singh, B., Chellappa, R., Davis, L.S.: Soft-NMS–improving object detection with one line of code. In: IEEE International Conference on Computer Vision, pp. 5562–5570. IEEE (2017)
Chen, T.: MXNet: a flexible and efficient machine learning library for heterogeneous distributed systems. arXiv (2015)
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Lei, J., Gao, C., Hu, J., Gao, C., Sang, N. (2019). Orientation Adaptive YOLOv3 for Object Detection in Remote Sensing Images. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11857. Springer, Cham. https://doi.org/10.1007/978-3-030-31654-9_50
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DOI: https://doi.org/10.1007/978-3-030-31654-9_50
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