Abstract
Wild searching and nature reserve monitoring are formidable tasks. In order to relieve the current pressure of general manpower observation, drone aerial surveillance using visible and thermal infrared (TIR) cameras is increasingly being adopted. Automatic data acquisition has become easier with advances in unmanned aerial vehicles (UAVs) and sensors like TIR cameras, which enables executives to search and detect ground objects at night. However, it’s still a challenge to accurately and quickly process the large amount of TIR data generated from this. In response to the above problems, this paper designs an enhanced ground object detection network (UAV-TIR Retinanet) for the UAV thermal imaging system. The network uses the Retinanet as infrastructure, extracts shallow features according to the characteristics of thermal infrared remote sensing images, introduces an attention mechanism and adaptive receptive field mechanism. The method achieves the best speed-accuracy trade-off on the dataset, reporting 74.47% AP at 23.48 FPS.
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References
He, Y., et al.: Infrared machine vision and infrared thermography with deep learning: a review. Infrared Phys. Technol. 2021, 103754 (2021)
Yao, H., Qin, R., Chen, X.: Unmanned aerial vehicle for remote sensing applications—a review. Remote Sens. 11(12), 1443 (2019)
Feng, L., et al.: A comprehensive review on recent applications of unmanned aerial vehicle remote sensing with various sensors for high-throughput plant phenotyping. Comput. Electron. Agricult. 182, 106033 (2021)
Rawat, S.S., Verma, S.K., Kumar, Y.: Review on recent development in infrared small target detection algorithms. Procedia Comput. Sci. 167, 2496–2505 (2020)
He, K., et al.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 37(9), 1904–1916 (2015)
Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision (2015)
Ren, S., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. Adv. Neural Inf. Process. Syst. 28, 91–99 (2015)
Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) Computer Vision – ECCV 2016. 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., et al.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision (2017)
Redmon, J., et al.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)
Kundid Vasić, M., Papić, V.: Multimodel deep learning for person detection in aerial images. Electronics 9(9), 1459 (2020)
Bondi, E., et al.: BIRDSAI: a dataset for detection and tracking in aerial thermal infrared videos. In: The IEEE Winter Conference on Applications of Computer Vision (2020)
Wu, Z., Shen, C., Van Den Hengel, A.: Wider or deeper: revisiting the resnet model for visual recognition. Pattern Recognit. 90, 119–133 (2019)
Wang, Q., et al.: ECA-Net: efficient channel attention for deep convolutional neural networks. In: 2020 IEEE in CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2020)
Lin, T.-Y., et al.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
Tan, M., Pang, R., Le, Q.V.: Efficientdet: Scalable and efficient object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020)
Luo, W., et al.: Understanding the effective receptive field in deep convolutional neural networks. In: Proceedings of the 30th International Conference on Neural Information Processing Systems (2016)
Liu, S., Huang, D., Wang, Y.: Receptive Field Block Net for Accurate and Fast Object Detection. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11215, pp. 404–419. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01252-6_24
Szegedy, C., et al.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-first AAAI Conference on Artificial Intelligence (2017)
Liu, J., et al.: High precision detection algorithm based on improved RetinaNet for defect recognition of transmission lines. Energy Rep. 6, 2430–2440 (2020)
Cartucho, J., Ventura, R., Veloso, M.: Robust object recognition through symbiotic deep learning in mobile robots. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE (2018)
Acknowledgements
We would like to express gratitude to the efforts of Bondi, Elizabeth and her team members for creating and making publicly available scientific data. We would also like to thank all the reviewers on their time and insightful comments which improved our manuscript.
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Wu, F., Zhou, G., He, J., Li, H., Liu, Y., Yang, G. (2021). Efficient Object Detection and Classification of Ground Objects from Thermal Infrared Remote Sensing Image Based on Deep Learning. In: Ma, H., et al. Pattern Recognition and Computer Vision. PRCV 2021. Lecture Notes in Computer Science(), vol 13022. Springer, Cham. https://doi.org/10.1007/978-3-030-88013-2_14
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DOI: https://doi.org/10.1007/978-3-030-88013-2_14
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