Abstract
Object detection in remote sensing images is a typical computer vision application, which has broad requirements in practice. Recently, attention mechanisms have been widely utilized in a diverse range of visual tasks such as object detection and semantic segmentation. Aimed at the characteristics of remote sensing objects such as rotation variations and inter-class similarity, a question we ask is, what kind of attention mechanism do we really need? In this article, we propose a novel attention refinement one-stage anchor-free object detector (AROA) that leverages attention mechanisms to refine the performance of remote sensing object detection in a one-stage anchor-free network framework. Specifically, we first design an asymmetric spatial self-attention (A\(\text {S}^2\)A) mechanism to capture rich long-range spatial contexts and eliminate the rotate distortion. Then, to solve the issue of inter-class similarity and boost the multiclass identification capability, we propose a channel attention mechanism, named chain-connected channel attention (\(\text {C}^3\)A), which connects the adjacent attention blocks like a chain and dramatically mines the channel relationships. In addition, we also introduce an IoU-wise module (IM) to strengthen the correlation between localization and classification branches and filter out the detected boxes with low positioning quality. Extensive experimental results on the DOTA and NWPU VHR-10 datasets demonstrate the effectiveness of the proposed AROA.
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References
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 2015, 91–99 (2015)
Yang, X., et al.: SCRDet: towards more robust detection for small, cluttered and rotated objects. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 8232–8241 (2019)
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
Lin, T.-Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)
Law, H., Deng, J.: CornerNet: detecting objects as paired keypoints. In: Proceedings of the European Conference on Computer Vision, pp. 734–750 (2018)
Tian, Z., Shen, C., Chen, H., He, T.: FCOS: fully convolutional one-stage object detection. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9627–9636 (2019)
Wei, H., Zhang, Y., Wang, B., Yang, Y., Li, H., Wang, H.: X-LineNet: detecting aircraft in remote sensing images by a pair of intersecting line segments. IEEE Trans. Geosci. Remote Sens. 9(2), 1645–1659 (2020)
Chen, J., Xie, F., Lu, Y., Jiang, Z.: Finding arbitrary-oriented ships from remote sensing images using corner detection. IEEE Geosci. Remote Sens. Lett. 17(10), 1712–1716 (2019)
Shi, F., Zhang, T., Zhang, T.: Orientation-aware vehicle detection in aerial images via an anchor-free object detection approach. IEEE Trans. Geosci. Remote Sens. 59(6), 5221–5233 (2020)
Li, Y., Huang, Q., Pei, X., Chen, Y., Jiao, L., Shang, R.: Cross-layer attention network for small object detection in remote sensing imagery. IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. 14, 2148–2161 (2020)
Wang, J., Wang, Y., Wu, Y., Zhang, K., Wang, Q.: FRPNet: a feature-reflowing pyramid network for object detection of remote sensing images. IEEE Geosci. Remote Sens. Lett. (2020)
Wu, Y., Zhang, K., Wang, J., Wang, Y., Wang, Q., Li, Q.: CDD-Net: a context-driven detection network for multiclass object detection. IEEE Geosci. and Remote Sens. Lett. (2020)
Zhang, X., Wang, G., Zhu, P., Zhang, T., Li, C., Jiao, L.: GRS-Det: an anchor-free rotation ship detector based on Gaussian-mask in remote sensing images. IEEE Trans. Geosci. Remote Sens. 59(4), 3518–3531 (2020)
Zhou, L., Wei, H., Li, H., Zhao, W., Zhang, Y., Zhang, Y.: Arbitrary-oriented object detection in remote sensing images based on polar coordinates. IEEE Access 8, 223373–223384 (2020)
Zhang, G., Lu, S., Zhang, W.: CAD-Net: a context-aware detection network for objects in remote sensing imagery. IEEE Trans. Geosci. Remote Sens. 57(12), 10015–10024 (2019)
Lin, Y., Feng, P., Guan, J.: IENet: interacting embranchment one stage anchor free detector for orientation aerial object detection (2019). arXiv:1912.00969
Wei, H., Zhang, Y., Chang, Z., Li, H., Wang, H., Sun, X.: Oriented objects as pairs of middle lines. ISPRS-J. Photogramm. Remote Sens. 169, 268–279 (2020)
Zhou, X., Wang, D., Krähenbühl, P.: Objects as points (2019). arXiv:1904.07850
Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794–7803 (2018)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
Xia, G., et al.: DOTA: a large-scale dataset for object detection in aerial images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3974–3983 (2018)
Cheng, G., Han, J.: A survey on object detection in optical remote sensing images. ISPRS-J. Photogramm. Remote Sens. 117, 11–28 (2016)
Acknowledgment
This work was supported in part by the National Natural Science Foundation of China under Grant 61701524, 62006245 and in part by the China Postdoctoral Science Foundation under Grant 2019M653742. Thanks to the anonymous reviewers for their valuable suggestions.
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He, X., Ma, S., He, L., Zhang, F., Liu, X., Ru, L. (2021). AROA: Attention Refinement One-Stage Anchor-Free Detector for Objects in Remote Sensing Imagery. In: Peng, Y., Hu, SM., Gabbouj, M., Zhou, K., Elad, M., Xu, K. (eds) Image and Graphics. ICIG 2021. Lecture Notes in Computer Science(), vol 12888. Springer, Cham. https://doi.org/10.1007/978-3-030-87355-4_23
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