Abstract
Human casualties in natural disasters have motivated technological innovations in Search and Rescue (SAR) activities. Difficult access to places where fires, tsunamis, earthquakes, or volcanoes eruptions occur has been delaying rescue activities. Thus, technological advances have gradually been finding their purpose in aiding to identify and find the best locations to put available resources and efforts to improve rescue processes. In this scenario, the use of Unmanned Aerial Vehicles (UAV) and Computer Vision (CV) techniques can be extremely valuable for accelerating SAR activities. However, the computing capabilities of this type of aerial vehicles are scarce and time to make decisions is also relevant when determining the next steps. In this work, we compare different Deep Learning (DL) imaging detectors for human detection in SAR images. A setup with drone-mounted cameras and mobile devices for drone control and image processing is put in place in Ecuador, where volcanic activity is frequent. The main focus is on the inference time in DL learning approaches, given the dynamic environment where decisions must be fast. Results show that a slim version of the model YOLOv3, while using less computing resources and fewer parameters than the original model, still achieves comparable detection performance and is therefore more appropriate for SAR approaches with limited computing resources.
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References
Rodin, C., et al.: Object classification in thermal images using convolutional neural networks for search and rescue missions with unmanned aerial systems. In: IEEE 2018 International Joint Conference on Neural Networks (IJCNN) (2018). https://doi.org/10.1109/IJCNN.2018.8489465
Yong, S., Yeong, Y.: Human object detection in forest with DL based on drone’s vision. In: 4th International Conference on Computer and Information Sciences (ICCOINS). IEEE (2018). https://doi.org/10.1109/ICCOINS.2018.8510564
Lygouras, E., et al.: Unsupervised human detection with an embedded vision system on a fully autonomous UAV for search and rescue operations. Sensors 19, 3542 (2019). https://doi.org/10.3390/s19163542
Tan, C., Sun, F., Kong, T., Zhang, W., Chang, C., Liu, C.: A survey on deep transfer learning (2018). arXiv:1808.01974v1
Blondel, P., Potelle, A., Pégard, C., Lozano, R.: Fast and viewpoint robust human detection for SAR operations. In: IEEE International Symposium on Safety, Security, and Rescue Robotics, Hokkaido, pp. 2374–3247 (2014). https://doi.org/10.1109/SSRR.2014.7017675
Rudol, P., Doherty, P.: Human body detection and geolocalization for UAV search and rescue missions using color and thermal imagery. In: IEEE Aerospace Conference, Big Sky, pp. 1095–323X (2008). https://doi.org/10.1109/AERO.2008.4526559
Oliveira, D., Wehrmeister, M.: Using deep learning and low-cost RGB and thermal cameras to detect pedestrians in aerial images captured by multirotor UAV. Sensors 18(7), 2444 (2018). https://doi.org/10.3390/s18072244
Zhang, P., Zhong, Y., Li, X.: SlimYOLOv3: narrower, faster and better for real-time (2019). arXiv:1907.11093v1
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN (2018). arXiv:1703.06870v3
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C., Berg, A.: Single shot multiBox detector (2015). arXiv:1512.02325v5
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016). https://doi.org/10.1109/CVPR.2016.91.
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Proceedings of the International Conference on Neural Information Processing Systems (NIPS 2015), Advances in Neural Information Processing Systems, vol. 28 (2015)
Llasag, R., Marcillo, D., Grilo, C., Silva, C.: Human detection for search and rescue applications with UAVs and mixed reality interfaces. In: 14th Iberian Conference on Information Systems and Technologies (CISTI), pp. 2166–0727 (2019). https://doi.org/10.23919/CISTI.2019.8760811
Khalaf, A., et al.: An architecture for simulating drones in mixed reality games to explore future search and rescue scenarios. In: Proceedings of the 15th International Conference on Information Systems, pp. 971–982 (2018)
Lin, T., et al.: Feature pyramid networks for object detection. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, pp. 1063–6919 (2017). https://doi.org/10.1109/CVPR.2017.106
Redmon, J., Farhadi, A.: YOLOv3: An incremental improvement (2018). arXiv:1804.02767v1
Lin, T., et al.: Microsoft COCO: common objects in context. In: European Conference on Computer Vision, LNCS, vol. 8693, pp. 740–755 (2014)
Sonntag, D., et al.: Fine-tuning deep CNN models on specific MS COCO categories (2017). arXiv:1709.01476v1
Russakovsky, O., Berg, A.C., Li, F.-F.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211–252 (2015)
Everingham, M., et al.: The pascal visual object classes challenge: a retrospective. Int. J. Comput. Vision 111(1), 98–136 (2015)
Luo, J.H., Wu, J.: An Entropy-based Pruning Method for CNN Compression (2018). arXiv:1706.05791
Wu, J., Leng, C., Wang, Y., Hu, Q., Cheng, J.: Quantized convolutional neural networks for mobile devices. In: IEEE Conference on Computer Vision and Pattern Recognition (2016). arXiv:1512.06473v3
Biswas, B., Bhattacharyya, S., Platos, J., Snasel, V.: Enhancement of dronogram aid to visual interpretation of target objects via intuitionistic fuzzy hesitant sets. Inf. Sci. 500(1), 67–86 (2019). https://doi.org/10.1016/j.ins.2019.05.069
ZhuEmail, P., et al.: VisDrone-DET2018: the vision meets drone object detection in image challenge results. In: Computer Vision - ECCV 2018 Workshops, LNCS, vol. 11133, pp. 437–468 (2018)
Lu, Z., Deb, K., Boddeti, V.N.: MUXConv: Information Multiplexing in Convolutional Neural Networks (2020). arXiv:2003.13880
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Rosero, R.L., Grilo, C., Silva, C. (2021). Deep Learning with Real-Time Inference for Human Detection in Search and Rescue. In: Abraham, A., Piuri, V., Gandhi, N., Siarry, P., Kaklauskas, A., Madureira, A. (eds) Intelligent Systems Design and Applications. ISDA 2020. Advances in Intelligent Systems and Computing, vol 1351. Springer, Cham. https://doi.org/10.1007/978-3-030-71187-0_23
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