OBJECT DETECTION AND DISTANCE MEASUREMENT
DOI:
https://doi.org/10.5281/zenodo.10824703Keywords:
YOLOv4-tiny; One-stage methods; Two-stage methods; autoencoder, CNN, DNN.Abstract
Object detection and distance measurement are fundamental tasks in computer vision, with applications ranging from autonomous vehicles to surveillance systems. This paper provides an overview of the various techniques and
technologies used for object detection and distance measurement, including their principles, advantages, and limitations. We discuss the importance of combining these two capabilities to extract valuable information for real-world applications. We used Yolo4 tiny for project. YOLOv4-tiny is the compressed version of YOLOv4. The YOLOv4-tiny model achieves 22.0% AP (42.0% AP50) at a speed of 443 FPS on RTX 2080Ti, while by using TensorRT, batch size = 4 and FP16- precision the YOLOv4-tiny achieves 1774 FPS. Moreover, in order to create project, we utilized range of methods such as autoencoder, CNN and DNN.
Downloads
References
Navaneeth Bodla, Bharat Singh, Rama Chellappa, and Larry S Davis. Soft-NMS–improving object detection with one line of code. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), pages 5561–5569, 2017.
Zhaowei Cai and Nuno Vasconcelos. Cascade R-CNN: Delving into high quality object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 6154–6162, 2018.
Jiale Cao, Yanwei Pang, Jungong Han, and Xuelong Li. Hierarchical shot detector. In Proceedings of the IEEE International Conference on Computer Vision (ICCV), pages 9705–9714, 2019.
Ping Chao, Chao-Yang Kao, Yu-Shan Ruan, Chien-Hsiang Huang, and Youn-Long Lin. HarDNet: A low memory traffic network. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2019.
Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L Yuille. DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 40(4):834– 848, 2017.
. Zheng, Q., Zhao, P., Zhang, D., Wang, H.: MR-DCAE: Manifold regularization-based deep convolutional autoencoder for unauthorized broadcasting identification. Int. J. Intell. Syst. (2021).
Yadav, S.: Deep learning based safe social distancing and face mask detection in public areas for covid-19 safety guidelines adherence. Int. J. Res. Appl. Sci. Eng. Technol. 8, 1–10 (2020)
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Doll´ar, and Ross Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 16000– 16009, 2022. 1, 2
Feng Li, Hao Zhang, Shilong Liu, Jian Guo, Lionel M Ni, and Lei Zhang. Dn-detr: Accelerate detr training by introducing query denoising. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 13619–13627, 2022. 2
Liunian Harold Li, Pengchuan Zhang, Haotian Zhang, Jianwei Yang, Chunyuan Li, Yiwu Zhong, Lijuan Wang, Lu Yuan, Lei Zhang, Jenq-Neng Hwang, et al. Grounded language-image pre-training. In Proceedings of the IEEE/CVF
Conference on Computer Vision and Pattern Recognition, pages 10965–10975, 2022. 1
Yanghao Li, Hanzi Mao, Ross Girshick, and Kaiming He. Exploring plain vision transformer backbones for object detection. arXiv preprint arXiv:2203.16527, 2022. 1
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Doll´ar, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In European conference on computer vision, pages
–755. Springer, 2014. 1, 2
Shilong Liu, Feng Li, Hao Zhang, Xiao Yang, Xianbiao Qi, Hang Su, Jun Zhu, and Lei Zhang. Dab-detr: Dynamic anchor boxes are better queries for detr. arXiv preprint arXiv:2201.12329, 2022. 2
Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, et al. Swin transformer v2: Scaling up capacity and resolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12009–12019, 2022. 1,
Downloads
Published
Issue
Section
License
License Terms of our Journal.
