Deep Learning for Pothole Detection on Indonesian Roadways
Main Article Content
Abstract
Accidents are common on Indonesian roadways. Accidents are caused by vehicles, motorcycles, and public transportation. Road fatalities are caused by speeding, alcohol, distraction, fatigue, and poor road conditions. There are numerous car accidents on Indonesian roadways. 30% of Indonesian traffic incidents are explained by road infrastructure and environmental conditions, 61% by driver skill and personality, and 9% by vehicle variables such as vehicle standardization. Cars are damaged, immobilized, and crashed as a result of road conditions. Every hour, three people pass away in traffic in Indonesia, according to authorities. According to the BPS's 2021 Land Transportation Statistics report, 31.91 percent of Indonesia's roads were damaged, totaling 174,298 kilometers. Accidents among Indonesian motorists are becoming more common as roads deteriorate. Using a single camera, a deep learning algorithm can recognize and detect road degradation such as potholes and road cracks. Train and process the model using transfer learning and fine-tuning on the Nano YOLOv5 model architecture. After being validated in three major scenarios, the model performs well with the appropriate confidence level. The precision metric for the model is 0.8, while recall and mAP:0.5 are both 0.5.
Article Details
How to Cite
Kusumah, H., Nurholik, M. R., Riani, C. P., & Nur Rahman, I. R. (2023). Deep Learning for Pothole Detection on Indonesian Roadways. Journal Sensi: Strategic of Education in Information System, 9(2), 175-186. https://doi.org/10.33050/sensi.v9i2.2911
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Articles
References
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[38] R. K. Al Romaihi, A. Belhi, and R. Hadjidj, “Evaluation of Single-Stage, Two-Stage, and Anchor-Free Object Recognition Methods for UAV Detection,” Lecture Notes in Networks and Systems, vol. 578, pp. 775–783, 2023, doi: 10.1007/978-981-19-7660-5_69/COVER.
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