Abstract
Different from natural images, remote sensing images in complex backgrounds have the challenges of small object feature extraction with small size, few available features, and high localization accuracy requirements. Consequently, it is difficult to detect these small objects with mainstream object detection methods. To enhance the retention of small object feature details during feature extraction and characteristics enhancement, this paper improves the Cascade R-CNN algorithm to solve the above problems of small object detection. Res2Net was used to enhance feature representation and feature fusion to extract features with fine-grained and rich semantic information. PAFPN was used for learning high-resolution features with large perceptual fields to perceive contextual information from the area around the object for inference. Cascade R-CNN was used as a high-quality detector by continuously increasing the threshold of IOU. Experiments show that the detection performance of the method proposed in this paper is 4.3% higher than that of the current mainstream deep learning methods, which is significantly better than other mainstream detection methods.
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References
Cai, Z., Vasconcelos, N.: Cascade r-cnn: high quality object detection and instance segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 43(5), 1483–1498 (2019)
Ding, J., Chen, B., Liu, H., Huang, M.: Convolutional neural network with data augmentation for sar target recognition. IEEE Geosci. Remote Sens. Lett. 13(3), 364–368 (2016)
Gao, S.H., Cheng, M.M., Zhao, K., Zhang, X.Y., Yang, M.H., Torr, P.: Res2net: a new multi-scale backbone architecture. IEEE Trans. Pattern Anal. Mach. Intell. 43(2), 652–662 (2019)
Gao, X., et al.: An end-to-end neural network for road extraction from remote sensing imagery by multiple feature pyramid network. IEEE Access 6, 39401–39414 (2018)
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2961–2969 (2017)
Li, Y., Han, S.-Y., Shen, Y., Liu, Z., Dong, J.-W., Xu, T.: Ujn-traffic: a benchmark dataset for performance evaluation of traffic element classification. In: Han, Q., McLoone, S., Peng, C., Zhang, B. (eds.) LSMS/ICSEE-2021. CCIS, vol. 1469, pp. 184–193. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-7213-2_18
Lim, J.S., Astrid, M., Yoon, H.J., Lee, S.I.: Small object detection using context and attention. In: 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp. 181–186. IEEE (2021)
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)
Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8759–8768 (2018)
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., Berg, A.C.: 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
Lu, X., Li, B., Yue, Y., Li, Q., Yan, J.: Grid R-CNN. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 7363–7372 (2019)
Mou, L., Zhu, X.X.: Vehicle instance segmentation from aerial image and video using a multitask learning residual fully convolutional network. IEEE Trans. Geosci. Remote Sens. 56(11), 6699–6711 (2018)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems 28 (2015)
Sun, P., et al.: Sparse R-CNN: end-to-end object detection with learnable proposals. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 14454–14463 (2021)
Tarel, J.P., Hautiere, N.: Fast visibility restoration from a single color or gray level image. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 2201–2208. IEEE (2009)
Wang, P., Sun, X., Diao, W., Fu, K.: Fmssd: Feature-merged single-shot detection for multiscale objects in large-scale remote sensing imagery. IEEE Trans. Geosci. Remote Sens. 58(5), 3377–3390 (2019)
Zhou, J., Chen, C.L.P., Chen, L., Li, H.X.: A collaborative fuzzy clustering algorithm in distributed network environments. IEEE Trans. Fuzzy Syst. 22(6), 1443–1456 (2014). https://doi.org/10.1109/TFUZZ.2013.2294205
Acknowledgments
This research was supported by the National Natural Science Foundation of China (61903156), the Natural Science Foundation of Shandong Province for Key Project (ZR2020KF006), the Shandong Provincial Key Research and Development Program (2018JMRH0102), Science and technology project of Department of Industry and Information Technology of Shandong Province (SJG2103), the Science and Technology Program of University of Jinan (XKY1928, XKY2001, XKY1803).
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Li, Y., Han, SY., Xu, T., Yang, Xh., Lu, Q., Shen, Y. (2023). Cascade Detector Based on Multi-scale Features for Small Objects in Various Backgrounds of Remote Sensing. In: You, P., Li, H., Chen, Z. (eds) Proceedings of International Conference on Image, Vision and Intelligent Systems 2022 (ICIVIS 2022). ICIVIS 2022. Lecture Notes in Electrical Engineering, vol 1019. Springer, Singapore. https://doi.org/10.1007/978-981-99-0923-0_31
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DOI: https://doi.org/10.1007/978-981-99-0923-0_31
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