Abstract
Automobile fuel tank cover recognition is one of the key technologies of intelligent unmanned fueling machine station system. A lightweight YOLOv8-based fuel cap detection algorithm is proposed to address the problem of recognizing and locating car fuel caps in unstructured environments. Considering the accuracy and real-time performance of the refueling task, the feature fusion network is firstly improved to two-scale feature detection, and then the detection accuracy and recognition speed are improved by combining with a weighted bidirectional feature fusion network (BiFPN); in order to further optimize the efficiency of the model’s feature extraction and to obtain feature information with stronger correlation, the C2f-Faster module is introduced to optimize the feature extraction module. The effectiveness of the lightweight improvement of the YOLOv8 model in fuel cap detection is verified through ablation experiments. The results show that the number of parameters of the method is reduced by 56.9% compared with the original YOLOv8 model, and the model complexity GFLOPs is reduced by 37%, while the detection speed and the detection accuracy are also improved, which demonstrates a good performance on the fuel cap detection task.
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References
Arokkiasamy, A., Sumathi, S.: Automation of petroleum filling stations using solid state controller. Adv. Nat. Appl. Sci. 11(6), 330–337 (2017)
Khan, F.S., Hossen, M.S., Islam, N., et al.: Smart fuel station controlling system. In: IOP Conference Series: Earth and Environmental Science, vol. 614, issue number 1, p. 012030 (2020)
Lam, C.T., Phung, T.C.: Research on application of industrial robots in automated fueling systems for small individual cars. Sci. Technol. Dev. J. Eng. Technol. 4(3), 1057–1067 (2021)
Becchi, F., Molfino, R.M., Razzoli, R.P.: Safety design of a petrol pump attendant robot. Ind. Robot Int. J. 32(5), 393–400 (2005)
Bi, Z., Luo, C., Miao, Z., et al.: Automatic robotic recharging systems–development and challenges. Ind. Robot Int. J. Robot. Res. Appl. 48(1), 95–109 (2021)
Wang, S., Ma, Z., Cui, H., et al.: Research on key algorithm of intelligent identification for refueling robot. In: 2017 14th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), pp. 61–65. IEEE, Chengdu (2017)
Zhang, X., Ma, M., Zhao, Y.: Oil tank cap detection based on mathematical morphology and Hough transformation. Manuf. Autom. 40(7), 119–122 (2018)
Liang, M.Y.: Research on Binocular Vision System of Automotive Refueling Robot. Master’s thesis, South China University of Technology (2021)
Ren, S., He, K., Girshick, R., et al.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Proceedings of the 28th International Conference on Neural Information Processing Systems, pp. 91–99. MIT Press, Cambridge (2015)
Zhang, Q., Li, Y.: Research on recognition method of car fuel tank cap based on residual network. J. Phys. Conf. Ser. 1748(4), 042017 (2021)
Ma, Z.: Research on Recognition and Detection Technology of Automobile Fuel Tank Lid Based on Binocular Vision. Doctoral dissertation, Jilin University (2022)
Redmon, J., Divvala, S., Girshick, R., et al.: You only look once: unified, real-time object detection. In: Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788. IEEE, Piscataway (2016)
Redmon, J., Farhadi, A.: YOLOv3: An Incremental Improvement. ar**v preprint ar**v:1804.02767 (2018)
Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: Yolov4: Optimal speed and accuracy of object detection. ar**v preprint ar**v:2004.10934 (2020)
Ultralytics: YOLOv8. https://docs.ultralytics.com/#ultralytics-yolov8. Accessed 15 July 2023
Chen, J.R., Kao, S.-h., He, H.: Run, Don’t Walk: Chasing Higher FLOPS for Faster Neural Networks. ar**v preprint ar**v:2303.03667 (2023)
Tan, M.X., Pang, R.M., Le, Q.V.: EfficientDet: Scalable and Efficient Object Detection. ar**v preprint ar**v:1911.09070 (2020)
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Fu, H., Hu, C., Guo, J., An, K. (2024). Research on Detection Method of Fuel Tank Cover Based on Lightweight YOLO. In: Qu, Y., Gu, M., Niu, Y., Fu, W. (eds) Proceedings of 3rd 2023 International Conference on Autonomous Unmanned Systems (3rd ICAUS 2023). ICAUS 2023. Lecture Notes in Electrical Engineering, vol 1176. Springer, Singapore. https://doi.org/10.1007/978-981-97-1099-7_33
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DOI: https://doi.org/10.1007/978-981-97-1099-7_33
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