Viewing on Google Maps Using Yolov8 for Damaged Traffic Signs Detection
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-1711-8_14
Reference17 articles.
1. Dewi, C., Chen, R.C., Jiang, X., Yu, H.: Deep convolutional neural network for enhancing traffic sign recognition developed on Yolo V4. Multimed. Tools Appl. 81(26), 37821–37845 (2022). https://doi.org/10.1007/s11042-022-12962-5
2. Dewi, C., Chen, R.C., Jiang, X., Yu, H.: Robust detection method for improving small traffic sign recognition based on spatial pyramid pooling. J. Ambient Intell. Human Comput. 14, 8135–8152 (2023). https://doi.org/10.1007/s12652-021-03584-0
3. Dewi, C., Chen, R.C., Zhuang, Y.C., Jiang, X., Yu, H.: Recognizing road surface traffic signs based on YOLO models considering image flips. Big Data and Cogn. Comput. 7(1), 54 (2023)
4. David, M., Matteo, B., Mario, V., Ratko, G.: Traffic sign detection using YOLO v3. In: Consumer Electronics, pp. 1–6 (2020)
5. Vinothkumar, S., Varadhaganapathy, S., Shanthakumari, R., Pradeev, S., Pragatheeswaran, S., Annamalai, K.S.: Traffic sign detection using hybrid network of YOLO and Resnet. In: Computer Communication and Informatics, pp. 1–7 (2023)
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