Advanced search
Publication Cover
Connection Science Volume 36, 2024 - Issue 1
Submit an article Journal homepage
403
Views
0
CrossRef citations to date
0
Altmetric
Research Article

A feature enhancement FCOS algorithm for dynamic traffic object detection

Tuqiang Zhoua School of Transportation Engineering, East China Jiaotong University, Nanchang, People’s Republic of ChinaView further author information
,
Wei Liua School of Transportation Engineering, East China Jiaotong University, Nanchang, People’s Republic of ChinaView further author information
&
Haoran Lib Suzhou Automotive Research Institute, Tsinghua University, Suzhou, People’s Republic of ChinaCorrespondence[email protected]
View further author information
Article: 2321345 | Received 26 Oct 2023, Accepted 15 Feb 2024, Published online: 01 Mar 2024

References

  • Bochkovskiy, A., Wang, C. Y., & Liao, H. Y. M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934.
  • Cai, Z., & Vasconcelos, N. (2018). Cascade R-CNN: Delving into high quality object detection. 2018 IEEE/CVF conference on Computer Vision and Pattern Recognition, 18-23 June, 6154–6162.
  • Cai, Z., & Vasconcelos, N. (2021). Cascade R-CNN: High quality object detection and instance segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(5), 1483–1498. https://doi.org/10.1109/TPAMI.2019.2956516
  • Cao, J., Cholakkal, H., Anwer, R. M., Khan, F. S., Pang, Y., & Shao, L. (2020). D2det: Towards high quality object detection and instance segmentation. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 14-19 June, 11482–11491.
  • Cao, J., Zhang, J., & Jin, X. (2021). A traffic-sign detection algorithm based on improved sparse R-CNN. IEEE Access, 9, 122774–122788. https://doi.org/10.1109/ACCESS.2021.3109606
  • Chen, W., Xiong, H., Li, K., & Li, X. (2018). Concurrent pedestrian and cyclist detection based on deep neural networks. Automotive Engineering, 40(6), 726–732.
  • Chen, Y., Xia, R., Yang, K., & Zou, K. (2023a). GCAM: Lightweight image inpainting via group convolution and attention mechanism. International Journal of Machine Learning and Cybernetics, 1–11.
  • Chen, Y., Xia, R., Yang, K., & Zou, K. (2023b). MFFN: Image super-resolution via multi-level features fusion network. The Visual Computer, 1–16.
  • Chen, Y., Xia, R., Yang, K., & Zou, K. (2023d). DGCA: High resolution image inpainting via DR-GAN and contextual attention. Multimedia Tools and Applications, 1–21.
  • Chen, Y., Xia, R., Yang, K., & Zou, K. (2023e). DARGS: Image inpainting algorithm via deep attention residuals group and semantics. Journal of King Saud University - Computer and Information Sciences, 35(6), 101567. https://doi.org/10.1016/j.jksuci.2023.101567
  • Chen, Y., Xia, R., Zou, K., & Yang, K. (2023c). RNON: Image inpainting via repair network and optimization network. International Journal of Machine Learning and Cybernetics, 1–17.
  • Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., & Tian, Q. (2019). Centernet: Keypoint triplets for object detection. 2019 IEEE/CVF international conference on computer vision, Seoul, Korea (South), 27 Oct-2 Nov, 6569–6578.
  • Gao, E., Huang, W., Shi, J., Wang, X., Zheng, J., Du, G., & Tao, Y. (2022). Long-tailed traffic sign detection using attentive fusion and hierarchical group softmax. IEEE Transactions on Intelligent Transportation Systems, 23(12), 24105–24115. https://doi.org/10.1109/TITS.2022.3200737
  • Gašparović, B., Lerga, J., Mauša, G., & Ivašić-Kos, M. (2022). Deep learning approach for objects detection in underwater pipeline images. Applied Artificial Intelligence, 36(1), 2146853. https://doi.org/10.1080/08839514.2022.2146853
  • Ge, Z., Liu, S., Wang, F., Li, Z., & Sun, J. (2021). Yolox: Exceeding yolo series in 2021[J]. arXiv preprint arXiv:2107.08430.
  • Girshick, R. (2015). Fast r-cnn. 2015 IEEE international conference on computer vision, 7-13 Dec, 1440–1448.
  • Guo, Y., Zhou, D., Li, P., Li, C., & Cao, J. (2022). Context-Aware poly (A) signal prediction model via deep spatial–temporal neural networks. IEEE Transactions on Neural Networks and Learning Systems, 1–13.
  • Hai, W., Kuan, W., Yingfeng, C., Ze, L., & Long, C. (2020). Traffic sign recognition based on improved cascade convolution neural network. Automotive Engineering, 42, 1256–1262.
  • He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask r-cnn. 2017 IEEE international conference on computer vision, 22-29 Oct, 2961–2969.
  • Hu, J., Shen, L., & Sun, G. (2018). Squeeze-and-excitation networks. 2018 IEEE conference on computer vision and pattern recognition, 18-23 June, 7132–7141.
  • Law, H., & Deng, J. (2018). Cornernet: Detecting objects as paired keypoints. 2018 European conference on computer vision (ECCV), 8-14 Sept, 734–750.
  • Li, W., Guo, Y., Wang, B., & Yang, B. (2023). Learning spatiotemporal embedding with gated convolutional recurrent networks for translation initiation site prediction. Pattern Recognition, 136, 109234. https://doi.org/10.1016/j.patcog.2022.109234
  • Liang, H. (2023). Improved EfficientDet algorithm for basketball players' upper limb movement trajectory recognition. Applied Artificial Intelligence, 37(1), 2225906. https://doi.org/10.1080/08839514.2023.2225906
  • Lin, T. Y., Dollár, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017a). Feature pyramid networks for object detection. 2017 IEEE conference on computer vision and pattern recognition, 21-26 July, 2117–2125.
  • Lin, T. Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017b). Focal loss for dense object detection. 2017 IEEE international conference on computer vision, 22-29 Oct, 2980–2988.
  • Liu, S., & Huang, D. (2018). Receptive field block net for accurate and fast object detection. 2018 European conference on computer vision (ECCV), 8-14 Sept, 385–400.
  • Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C., & C. Berg, A. (2016). Ssd: Single shot multibox detector. Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, 11–14 Oct, 2016, Proceedings, Part I 14. Springer International Publishing, 21–37.
  • Liu, X., Ghazali, K. H., Han, F., & Mohamed, I. I. (2021). Automatic detection of oil palm tree from UAV images based on the deep learning method. Applied Artificial Intelligence, 35(1), 13–24. https://doi.org/10.1080/08839514.2020.1831226
  • Luo, J., Fang, H., Shao, F., Hu, C., & Meng, F. (2021). Vehicle detection in congested traffic based on simplified weighted dual-path feature pyramid network with guided anchoring. IEEE Access, 9, 53219–53231. https://doi.org/10.1109/ACCESS.2021.3069216
  • Papandreou, G., Zhu, T., Chen, L. C., Gidaris, S., Tompson, J., & Murphy, K. (2018). Personlab: Person pose estimation and instance segmentation with a bottom-up, part-based, geometric embedding model. 2018 European conference on computer vision (ECCV), 8-14 Sept, 269–286.
  • Rampriya, R. S., Suganya, R., Nathan, S., & Perumal, P. S. (2022). A comparative assessment of deep neural network models for detecting obstacles in the real time aerial railway track images. Applied Artificial Intelligence, 36(1), 2018184. https://doi.org/10.1080/08839514.2021.2018184
  • Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. 2016 IEEE conference on computer vision and pattern recognition, 27-30 June, 779–788.
  • Redmon, J., & Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767.
  • Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems, 91–99.
  • Shi, H., Lai, R., Li, G., & Yu, W. (2022). Visual inspection of surface defects of extreme size based on an advanced FCOS. Applied Artificial Intelligence, 36(1), 2122222. https://doi.org/10.1080/08839514.2022.2122222
  • Song, G., Liu, Y., & Wang, X. (2020). Revisiting the sibling head in object detector. 2020 IEEE/CVF conference on computer vision and pattern recognition, 14-19 June, 11563–11572.
  • Song-tao, D., & Shi-ru, Q. U. (2018). Traffic object detection based on deep learning with region of interest selection. China Journal of Highway and Transport, 31(9), 167–174.
  • Tang, Q., Liu, S., Li, J., Hu, Y. (2019). PosNeg-balanced anchors with aligned features for single-shot object detection. arXiv preprint arXiv:1908.03295.
  • Tian, Z., Shen, C., Chen, H., He, T. (2019). Fcos: Fully convolutional one-stage object detection. 2019 IEEE/CVF international conference on computer vision, 27 Oct-2 Nov, 9627–9636.
  • Vu, T., Jang, H., Pham, T. X., & Yoo, C. D. (2019). Cascade rpn: Delving into high-quality region proposal network with adaptive convolution. Advances in Neural Information Processing Systems, 1432–1442.
  • Wang, K., & Zhang, L. (2020). Single-shot two-pronged detector with rectified iou loss. 2020 28th ACM International Conference on Multimedia, 12 Oct, 1311–1319.
  • Yan, L., Jia, L., Lu, S., Peng, L., & He, Y. (2023). LSTM-based deep learning framework for adaptive identifying eco-driving on intelligent vehicle multivariate time-series data. IET Intelligent Transport Systems, 00, 1–17.
  • Yang, B., Bender, G., Le, Q. V., et al. (2019). Condconv: Conditionally parameterized convolutions for efficient inference. Advances in Neural Information Processing Systems, 1307–1318.
  • Yang, S., Yunpeng, L., & Yu, L. (2022a). Lane detection based on instance segmentation of BiSeNet V2 backbone network. Applied Artificial Intelligence, 36(1), 2085321. https://doi.org/10.1080/08839514.2022.2085321
  • Yang, S., Zhou, D., Cao, J., & Guo, Y. (2022b). Rethinking low-light enhancement via transformer-GAN. IEEE Signal Processing Letters, 29, 1082–1086. https://doi.org/10.1109/LSP.2022.3167331
  • Yang, S., Zhou, D., Cao, J., & Guo, Y. (2023). LightingNet: An integrated learning method for low-light image enhancement. IEEE Transactions on Computational Imaging, 9, 29–42. https://doi.org/10.1109/TCI.2023.3240087
  • Yuan, S., Wang, K., Shan, Y., & Yang, J. (2021). Multi-scale object detection method based on multi-branch parallel dilated convolution. Journal of Computer-Aided Design & Computer Graphics, 33(6), 864–872. https://doi.org/10.3724/SP.J.1089.2021.18537
  • Zhong, Q., Li, C., Zhang, Y., Xie, D., Yang, S., & Pu, S. (2020). Cascade region proposal and global context for deep object detection. Neurocomputing, 395, 170–177. https://doi.org/10.1016/j.neucom.2017.12.070
  • Zhou, T., Liu, W., Zhang, M., & Jia, J. (2023). Optimization of AEB decision system based on unsafe control behavior analysis and improved ABAS algorithm. IEEE Transactions on Intelligent Transportation Systems, 1–14.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.