Abstract
Light detection and ranging sensors (LIDAR) play a fundamental role in the perception systems of Unmanned Ground Vehicles. However, particulate matters existing in smog scenarios such as fire disasters seriously degrade the robustness of LIDAR-based algorithms. Therefore, it’s essential for UGV to recognize and locate smog. Based on the Class Activation Map (CAM), we propose a lightweight smog detection model, where an active annotation method combining with activation loss is designed for enhancing the quality of CAM. Experiments demonstrate the effectiveness of the proposed method with just 4ms time costs on GTX-1050ti.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Nabati, R., Qi, H.: CenterFusion: center-based radar and camera fusion for 3D object detection. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1527–1536 (2021)
Wang, Y., et al.: Pillar-based object detection for autonomous driving. arXiv preprint arXiv:2007.10323 (2020)
Chen, X., Ma, H., Wan, J., Li, B., Xia, T.: Multi-view 3D object detection network for autonomous driving. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 1907–1915 (2017)
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017)
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921–2929 (2016)
Wang, Y., Chua, T.W., Chang, R., Pham, N.T.: Real-time smoke detection using texture and color features. In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), pp. 1727–1730. IEEE, November 2012
Jia, Y., Yuan, J., Wang, J., Fang, J., Zhang, Q., Zhang, Y.: A saliency-based method for early smoke detection in video sequences. Fire Technol. 52(5), 1271–1292 (2016)
Gunay, O., Toreyin, B.U., Kose, K., Cetin, A.E.: Entropy-functional-based online adaptive decision fusion framework with application to wildfire detection in video. IEEE Trans. Image Process. 21(5), 2853–2865 (2012)
Liu, Z., Yang, X., Liu, Y., Qian, Z.: Smoke-detection framework for high-definition video using fused spatial-and frequency-domain features. IEEE Access 7, 89687–89701 (2019)
Yuan, F., Shi, J., Xia, X., Zhang, L., Li, S.: Encoding pairwise Hamming distances of local binary patterns for visual smoke recognition. Comput. Vis. Image Underst. 178, 43–53 (2019)
Favorskaya, M., Pyataeva, A., Popov, A.: Verification of smoke detection in video sequences based on spatio-temporal local binary patterns. Procedia Comput. Sci. 60, 671–680 (2015)
Frizzi, S., Kaabi, R., Bouchouicha, M., Ginoux, J.M., Moreau, E., Fnaiech, F.: Convolutional neural network for video fire and smoke detection. In: IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society, pp. 877–882. IEEE, October 2016
Cheng, S., Ma, J., Zhang, S.: Smoke detection and trend prediction method based on Deeplabv3+ and generative adversarial network. J. Electron. Imaging 28(3), 033006 (2019)
Yang, Z., Shi, W., Huang, Z., Yin, Z., Yang, F., Wang, M.: Combining Gaussian mixture model and HSV model with deep convolution neural network for detecting smoke in videos. In: 2018 IEEE 18th International Conference on Communication Technology (ICCT), pp. 1266–1270. IEEE, October 2018
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)
Acknowledgement
This work is supported by the National Natural Science Foundation of China (NSFC) under Grants 61825305 and 61973311.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sun, Y., Hu, X., Xu, X., Li, J. (2022). Lightweight Smog Detection Model for Unmanned Ground Vehicle Based on Interpretability of Neural Networks. In: Wu, M., Niu, Y., Gu, M., Cheng, J. (eds) Proceedings of 2021 International Conference on Autonomous Unmanned Systems (ICAUS 2021). ICAUS 2021. Lecture Notes in Electrical Engineering, vol 861. Springer, Singapore. https://doi.org/10.1007/978-981-16-9492-9_9
Download citation
DOI: https://doi.org/10.1007/978-981-16-9492-9_9
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-9491-2
Online ISBN: 978-981-16-9492-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)