Abstract
Mine pedestrian detection is an important part of computer vision and one of the key technologies of unmanned locomotive. In order to improve the structural adaptability of pedestrian detection network, reduce the workload of pre-training and reduce the risk of “negative migration” brought by migration learning, a non-pre-training underground pedestrian detection network based on anchor box is proposed. Firstly, the network model of mine pedestrian detection is introduced, including non-pretrained backbone network and branch structure detection network. Among them, the non-pre-trained backbone network mainly adds BatchNorm operation to make the gradient more stable and smooth. Anchor location prediction branch and anchor shape prediction branch in the detection network work together to improve the regression accuracy of anchor box. Secondly, the loss function of network training is described and the training parameters are adjusted by weighted loss. Finally, the experimental results based on the video of Taoyuan and Xinji mine in Anhui province are given. The experimental data show that the proposed algorithm can still maintain 96.3% AP at a real-time processing rate of 24 FPS. Compared with the RefineDet512, the AP increases by 2.4%.
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References
Lin, T.Y., Dollár, P., Girshick, R., et al.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2117–2125 (2017)
Krishnamurthy, B., Sarkar, M.: Deep-learning network architecture for object detection. US Patent 10,152,655, 11 Dec 2018
Zhu, R., Zhang, S., Wang, X., et al.: ScratchDet: training single-shot object detectors from scratch. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2268–2277 (2019)
Anantaram, C., Kopparapu, S.K., Patel, C.R., et al.: Systems and methods for automatic repair of speech recognition engine output using a sliding window mechanism. US Patent 10,410,622, 10 September 2019
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv: Learning (2015)
Ren, S., He, K., Girshick, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Neural Information Processing Systems, pp. 91–99 (2015)
He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Redmon, J., Divvala, S.K., Girshick, R., et al.: 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)
Liu, W., et al.: 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
Lin, T., Goyal, P., Girshick, R., et al.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2999–3007 (2017)
Zhou, X., Wang, D., Krahenbuhl, P., et al.: Objects as points. Computer Vision and Pattern Recognition. arXiv (2019)
Zhao, Q., Sheng, T., Wang, Y., et al.: M2Det: a single-shot object detector based on multi-level feature pyramid network. In: National Conference on Artificial Intelligence, vol. 33, no. 01, pp. 9259–9266 (2019)
Acknowledgments
This work was supported by Anhui Provincial Key R&D Program (201904d08020040) and National Key R&D Program of China (2018F YC0604404).
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Wei, X., Wang, C., Zhang, H., Liu, S., Lu, Y. (2020). Non-pre-trained Mine Pedestrian Detection Based on Automatic Generation of Anchor Box. In: Yu, D., Dressler, F., Yu, J. (eds) Wireless Algorithms, Systems, and Applications. WASA 2020. Lecture Notes in Computer Science(), vol 12385. Springer, Cham. https://doi.org/10.1007/978-3-030-59019-2_14
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