Mobile QR Code QR CODE

REFERENCES

1 
Albelwi, S., & Mahmood, A. (2017). A framework for designing the architectures of deep convolutional neural networks. Entropy, 19(6), 242.DOI
2 
Brahim, J., Khalid El, M., & Noureddine, F. (2023). Developing an Efficient System with Mask R-CNN for Agricultural Applications. AGRIS on-line Papers in Economics and Informatics, 15(1), 61 - 72.DOI
3 
Gu, Y., & Si, B. (2022). A novel lightweight real-time traffic sign detection integration framework based on YOLOv4. Entropy, 24(4), 487.DOI
4 
Hameed, K., Chai, D., & Rassau, A. (2022). Score-based mask edge improvement of Mask-RCNN for segmentation of fruit and vegetables. Expert Systems with Applications, 190, 116205.DOI
5 
He, P., Zuo, L., Zhang, C., & Zhang, Z. (2019). A value recognition algorithm for pointer meter based on improved Mask-RCNN. 9th International Conference on Information Science and Technology (ICIST), (pp. 108-113). Hulunbuir, China.DOI
6 
Hussien, R. S., Elkhidir, A. A., & Elnourani, M. (2015). Optical character recognition of Arabic handwritten characters using neural network. 2015 International Conference on Computing, Control, Networking, Electronics and Embedded Systems Engineering (ICCNEEE), (pp. 456-461).DOI
7 
Hyder, A. A., Norton, R., Pérez-Núñez, R., Mojarro-Iñiguez, F. R., Peden, M., Kobusingye, O., et al. (2016). The Road Traffic Injuries Research Network: a decade of research capacity strengthening in low- and middle-income countries. Health Res Policy Sys , 14(14), 1-9.DOI
8 
Jain, S. (2020). Pushing the boundary of Semantic Image Segmentation. ETH Zurich: KTH, School of Electrical Engineering and Computer Science (EECS).URL
9 
Kattenborn, T., Leitloff, J., Schiefer, F., & Hinz , S. (2021). Review on Convolutional Neural Networks (CNN) in vegetation remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 173, 24-49.DOI
10 
Kesav, N., & Jibukumar, M. G. (2022). Efficient and low complex architecture for detection and classification of Brain Tumor using RCNN with Two Channel CNN. Journal of King Saud University - Computer and Information Sciences, 34(8), 6229-6242.DOI
11 
Khan, J. F., Bhuiyan, S. A., & Adhami, R. R. (2011). Image segmen-tation and shape analysis for road-sign detection. IEEE Transactions on Intelligent Transportation Systems, 12(1), 83-96.DOI
12 
Li, W. (2021). Analysis of object detection performance based on Faster R-CNN. 6th International Conference on Electronic Technology and Information Science (ICETIS 2021), 1827. Harbin, China.DOI
13 
Mammeri, A., Khiari, E. H., & Boukerche, A. (2014). Road-sign text recognition architecture for intelligent transportation systems. IEEE 80th Vehicular Technology Conference (VTC2014-Fall), (pp. 1-5). Vancouver, BC, Canada.DOI
14 
Mehta, S., Paunwala, C., & Vaidya, B. (2019). CNN based traffic sign classification using adam optimizer. 2019 International Conference on Intelligent Computing and Control Systems (ICCS), (pp. 1293-1298). Madurai, India.DOI
15 
Mogelmose, A., Trivedi, M. M., & Moeslund, T. B. (2012). Vision-Based Traffic Sign Detection and Analysis for Intelligent Driver Assistance Systems: Perspectives and Survey. IEEE Transactions on Intelligent Transportation Systems, 13, pp. 1484-1497.DOI
16 
Qin, F., Fang, B., & Zhao, H. (2010). Traffic sign segmentation and recognition in scene images. 2010 Chinese Conference on Pattern Recognition (CCPR), (pp. 1-5). Chongqing, China.DOI
17 
Reinius, S. (2013, 1 30). Object recognition using the OpenCV Haar cascade-classifier on the iOS platform. Institutionen för informationsteknologi, Department of Information Technology, Uppsala Universitet.URL
18 
Robielos, R., & Lin, C. J. (2022). Traffic Sign Comprehension among Filipino Drivers and Nondrivers in Metro Manila. Appl. Sci., 12(16), 8337.DOI
19 
Sai, B. N., & Sasikala, T. (2019, February). Object detection and count of objects in image using tensor flow object detection API. 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT), (pp. 542-546). Tirunelveli, India.DOI
20 
Wang, Y., Jiang, Z., Li, Y., Hwang, J. N., & Xing an, G. (2021). RODNet: A Real-Time Radar Object Detection Network Cross-Supervised by Camera-Radar Fused Object 3D Localization. IEEE Journal of Selected Topics in Signal Processing, pp. (99):1-1.DOI
21 
Wu, J., & Liao, S. (2022). Traffic sign detection based on SSD combined with receptive field module and path aggregation network. Computational Intelligence and Neuroscience, Hindawi, 2022, 1-13.DOI
22 
Yu, K., Hao, Z., Post, C. J., Mikhailova, E. A., Lin , L., Zhao, G., et al. (2022). Comparison of classical methods and mask R-CNN for automatic tree detection and mapping using UAV imagery. Remote Sensing, 14(2), 295.DOI
23 
Zhu, Y., Xu , T., Peng, L., Cao, Y., Zhao, X., Li, S., et al. (2022). Faster-RCNN based intelligent detection and localization of dental caries. Displays, 74, 102201.DOI