Abstract
In this paper, we present our approach for drone detection which we submitted for the Drone-Vs-Bird Detection Challenge. In our work, we used the Fully Convolutional One-Stage Object Detection (FCOS) approach tuned to detect drones. Throughout our experiments, we opted for a simple data augmentation technique to reduce the amount of False Positives (FPs). Upon observing the results of our early experiments, our technique for data augmentation incorporates adding extra samples to the training sets including the object which generated the most number of FPs, namely other flying objects, leaves and objects with sharp edges. With the newly introduced data to the training set, our results for drone detection on the validation set are as follows: AP scores of 0.16, 0.34 and 0.65 for small-sized, medium-sized and large drones respectively.
The first three authors equally contributed to this work.
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References
Aker, C., Kalkan, S.: Using deep networks for drone detection. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6. IEEE (2017)
Coluccia, A., et al.: Drone-vs-bird detection challenge at IEEE AVSS2021. In: 2021 17th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–8. IEEE (2021)
Coluccia, A., et al.: Drone vs. bird detection: deep learning algorithms and results from a grand challenge. Sensors 21(8), 2824 (2021)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
Magoulianitis, V., Ataloglou, D., Dimou, A., Zarpalas, D., Daras, P.: Does deep super-resolution enhance UAV detection? In: 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6. IEEE (2019)
Nalamati, M., Kapoor, A., Saqib, M., Sharma, N., Blumenstein, M.: Drone detection in long-range surveillance videos. In: 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6. IEEE (2019)
Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263–7271 (2017)
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)
Saqib, M., Khan, S.D., Sharma, N., Blumenstein, M.: A study on detecting drones using deep convolutional neural networks. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–5. IEEE (2017)
Schumann, A., Sommer, L., Klatte, J., Schuchert, T., Beyerer, J.: Deep cross-domain flying object classification for robust UAV detection. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6. IEEE (2017)
Schumann, A., Sommer, L., Müller, T., Voth, S.: An image processing pipeline for long range UAV detection. In: Emerging Imaging and Sensing Technologies for Security and Defence III; and Unmanned Sensors, Systems, and Countermeasures, vol. 10799, p. 107990T. International Society for Optics and Photonics (2018)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)
Sommer, L., Schumann, A., Müller, T., Schuchert, T., Beyerer, J.: Flying object detection for automatic UAV recognition. In: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6. IEEE (2017)
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818–2826 (2016)
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)
Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1492–1500 (2017)
Yamanaka, J., Kuwashima, S., Kurita, T.: Fast and accurate image super resolution by deep CNN with skip connection and network in network. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, E.S. (eds.) Neural Information Processing, pp. 217–225. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70096-0_23
Zeiler, M.D., Fergus, R.: Visualizing and understanding convolutional networks. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8689, pp. 818–833. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10590-1_53
Acknowledgements
The work was partially supported by The University of Tokyo. The first author conducted this work as part of the NII International Internship Program.
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Nayak, A. et al. (2022). Evaluation of Fully Convolutional One-Stage Object Detection for Drone Detection. In: Mazzeo, P.L., Frontoni, E., Sclaroff, S., Distante, C. (eds) Image Analysis and Processing. ICIAP 2022 Workshops. ICIAP 2022. Lecture Notes in Computer Science, vol 13374. Springer, Cham. https://doi.org/10.1007/978-3-031-13324-4_37
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