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Infrared Image Object Detection of Vehicle and Person Based on Improved YOLOv5

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Web and Big Data. APWeb-WAIM 2022 International Workshops (APWeb-WAIM 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1784))

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Abstract

Existing object detection algorithms are difficult to perform object detection tasks on embedded devices under the limitations of energy efficiency ratio and power consumption due to complex network structure and huge computational and parametric quantities. The object detection task in infrared images has low recognition rate and high false alarm rate due to long distance, weak energy and low resolution. In order to achieve the detection task at the mobile edge of infrared vehicle pedestrian target detection, this paper puts the YOLOv5 algorithm into a series of optimizations and proposes a lightweight YOLO-mini network structure. That is, instead of CSPDarknet, the MobileNetV2 network structure is used as the backbone feature extraction network with the addition of coordinate attention mechanism. Also, to make the network model more lightweight, the weights are converted to int8 type by quantized sensing training, which enables the task of the object detection algorithm for infrared vehicle pedestrian dataset on embedded devices. Experiments testing the FLIR dataset on NVIDIA Xavier NX show that this algorithm greatly reduces the number of network model parameters with less loss of accuracy and improves the FPS. mAP of YOLO-MobileNetV2 reaches 86.75%, number of parameters 2.76M, and FPS of 45; The network structure of YOLO-mini achieves 84.63% mAP, 0.69M number of parameters, and 63 FPS.

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References

  1. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of IEEE Conference on Computer Vision and PatternRecognitio, pp. 770–778 (2016)

    Google Scholar 

  2. Zhu, H., Qin, L., Sun, B.: Review on parallelization of deep neural networks. J. Chin. J. Computer. 41(8), 171–191 (2018). https://doi.org/10.11897/SP.J.1016.2018.01861

  3. Krizhevsky, I.S., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Proceedings of Advance Neural Information and Processing Systems, pp. 1097–1105 (2021)

    Google Scholar 

  4. Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: Proceedings of International Conference on Machine Learning, pp. 807–814 (2010)

    Google Scholar 

  5. Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing coadaptation of feature detectors. arXiv:1207.0580 (2012)

  6. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)

    Google Scholar 

  7. Girshick, R., Donahue, J., Darrell, T., et al.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587. IEEE Computer Society (2014)

    Google Scholar 

  8. Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)

    Google Scholar 

  9. Ren, S., He, K.,Girshick, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Proceedings of the 2015 Advances in Neural Information Processing Systems, pp. 91–99 (2015)

    Google Scholar 

  10. Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) Computer Vision – ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2

    Chapter  Google Scholar 

  11. Redmon, J.,Divvala, S., Girshick, R., et al.: You Only look once: unified, real time object detection. In: Computer Vision and Pattern Recognition, pp. 6517–6525 (2017)

    Google Scholar 

  12. Redmon, J., Farhadi, A.: YOLO 9000: better, faster, stronger. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 6517–6525 (2017)

    Google Scholar 

  13. Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. In: IEEE Conference on Computer Vision and Pattern Recognition.arXiv:1804.0276 (2018)

  14. Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: YOLOv4: optimal speed and accuracy of object detection. In: IEEE Conference on Computer Vision and Pattern Recognition. arXiv:2004.10934v1 (2020)

  15. Jocher, G., Stoken, A., Borovec, J.: Ultralytics/yolov5: V4. 0-Nn. SiLU () activations weights & biases logging PyTorch hub integration. Zenodo, Techical report. https://zenodo.org/record/4418161. https://doi.org/10.5281/zenodo.4418161(2021)

  16. Sandler, M., Howard, A., Zhu, M., et al.: MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)

    Google Scholar 

  17. Fang, L., Wang, X., Wan, Y.: Adaptable active contour model with applications to infrared ship target segmentation. J. Elect. Imaging 25(4), 1–10 (2016). https://doi.org/10.1117/1.JEI.25.4.041010

    Article  Google Scholar 

  18. Zhao, K., Kong, X.: Background noise suppression in small targets infrared images and its method discussion. Opt. Optoelectron. Technol. 2, 9–12 (2004)

    Google Scholar 

  19. Anju, T.S., Raj, N.R.N.: Shearlet transform based image denoisingusing histogram thresholding. In: Proceedings of International Conference on Communication System Network (ComNet), July 2016, pp. 162–166 (2016)

    Google Scholar 

  20. Jiao, P.: Research on image classification and retrieval method based on deep learning and sparse representation. M.S. thesis, Xi’an University Technology, Xi’an, China (2019)

    Google Scholar 

  21. Choi, Y., et al.: KAIST multi-spectral day/night data set for autonomous and assisted driving. IEEE Trans. Intell. Transp. Syst. 19(3), 934–948 (2018). https://doi.org/10.1109/TITS.2018.2791533

    Article  Google Scholar 

  22. FREE FLIR: Thermal Dataset for Algorithm Training. https://www.flir.in/oem/adas/adas-dataset-form

  23. Ariffin, S.M., Jamil, N., Rahman, P.N.: DIAST variability illuminated thermal and visible ear images datasets. In: Proceedings of Signal Processing, Algorithms, Architecture, Arrangements, Application (SPA), September 2016, pp. 191–195 (2016)

    Google Scholar 

  24. Li, M., Zhang, T., Cui, W.: Research of infrared small pedestrian target detection based on YOLOv3. Infr. Technoiogy 42(2), 176–181 (2020)

    Article  Google Scholar 

  25. Li, Y., Li, S., Du, H., Chen, L., Zhang, D., Li, Y.: YOLO-ACN: focusing on small target and occluded object detection. IEEE Access 8, 227288–227303 (2020)

    Article  Google Scholar 

  26. Cao, Y., Zhou, T., Zhu, X.,Su, Y.: Every feature counts: an improved one-stage detector in thermal imagery. In: Proceedings of IEEE 5th International Conference Computer Communication, (ICCC), December 2019, pp. 1965–1969 (2019)

    Google Scholar 

  27. Song, X., Gao, S., Chen, C.: A multispectral feature fusion net-work for robust pedestrian detection. Alexandria Eng. J. 60(1), 73–85 (2021). https://www.sciencedirect.com/science/article/pii/S1110016820302507

  28. Hou, Q., Zhou, D., Feng, J.: Coordinate attention for efficient mobile network design. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 13708–13717 (2021).https://doi.org/10.1109/CVPR46437.2021.01350

  29. Jacob, B., Kligys,S., Chen, B., et al.: Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference (2017)

    Google Scholar 

  30. Devaguptapu, C., Akolekar, N., Sharma, M.M., Balasubramanian, V.N.: Borrow from anywhere: pseudo multi-modal object detection in thermal imagery. In: Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1–10 (2019)

    Google Scholar 

  31. Cao, Y., Zhou, T., Zhu, X., Su, Y.: Every featurecounts: an improved one-stage detector in thermal imagery. In: Proceedings IEEE 5th International Conference on Computer Communication (ICCC), pp. 1965–1969 (2019)

    Google Scholar 

  32. Zhang, S., Wen, L., Bian, X., Lei, Z., Li, S.Z.: Single-shot refinement neural network for object detection. In: Proceedings of IEEE/CVF Conference on Computer Vision Pattern Recognition, pp. 4203–421 (2018)

    Google Scholar 

  33. Li, S., Li, Y., Li, Y., Li, M., Xu, X.: YOLO-FIRI: Improved YOLOv5 for infrared image object detection. IEEE Access. 9, 141861–141875 (2021)

    Google Scholar 

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Authors and Affiliations

  1. School of Software, Tiangong University, Tianjin, 300387, China

    Jintao Wang

  2. School of Computer Science and Technology, Tiangong University, Tianjin, 300387, China

    Qingzeng Song & Maorui Hou

  3. Beijing Polytechnic, Beijing, 100176, China

    Guanghao Jin

Authors
  1. Jintao Wang
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  2. Qingzeng Song
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  3. Maorui Hou
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  4. Guanghao Jin
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Corresponding author

Correspondence to Guanghao Jin .

Editor information

Editors and Affiliations

  1. Guangzhou University, Guangzhou, China

    Shiyu Yang

  2. Griffith University, Gold Coast, QLD, Australia

    Saiful Islam

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Wang, J., Song, Q., Hou, M., Jin, G. (2023). Infrared Image Object Detection of Vehicle and Person Based on Improved YOLOv5. In: Yang, S., Islam, S. (eds) Web and Big Data. APWeb-WAIM 2022 International Workshops. APWeb-WAIM 2022. Communications in Computer and Information Science, vol 1784. Springer, Singapore. https://doi.org/10.1007/978-981-99-1354-1_16

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  • DOI: https://doi.org/10.1007/978-981-99-1354-1_16

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-1353-4

  • Online ISBN: 978-981-99-1354-1

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