Photo: Siroga Team with their supervisors taking a photo in front of the Robotics Lab
PNM – Passing funding for the 2024 Vocational Student Creativity Program (PKM) Funding Scheme last April, the Siroga Team, which consists of students from the Computer Control Engineering (TKK) Study Program at Madiun State Polytechnic (PNM), innovated by creating tools to support sustainable transportation infrastructure. This innovation is entitled "Deep Learning-Based Flexible Pavement Damage Identification Tool and Surface Distress Index Efforts to Support Sustainable Transportation Infrastructure".
The development of the above tools is part of the PKM Karsa Cipta scheme (PKM-KC). Through PKM-KC, students are required to develop creativity in creating innovative new products or systems.
This tool was developed by the Siroga Team whose members were Arfian Iqbal Fatkhulhuda, Haninditio Bagus Septiansyah, Ridho Esa Prayoga, Farich Aswa Huda, and Ririn Eka Wahyu Putriana. The TKK Study Program students were also accompanied by supervisor Sulfan Bagus Setyawan, S.ST., M.T. in developing innovation.
Siroga Team Leader, Arfian Iqbal Fatkhulhuda said that damage to the surface layer of road pavement is one of the causes of traffic accidents. According to the Traffic Directorate of Polda Metro Jaya, more than 20 accident cases were caused by damaged roads or potholes, with details of 12 cases due to damaged roads and 15 cases due to potholes.
"Apart from causing traffic accidents, road damage also has other negative impacts, including increased operational costs, inconvenience to road users and excessive gas emissions." Arfian said.
Furthermore, the Siroga Team has also exchanged opinions with the Head of the Highways Division of the Ngawi Regency Public Works Service, Rachmat Fitrianto, S,T. regarding the tool to be created. Considering the high number of accident cases and the negative impacts resulting from damage to the road pavement layer, efforts are needed to reduce these problems.
Photo: The tool system created by the Siroga Team
For this reason, the Madiun State Polytechnic Siroga Team designed a system "Flexible Pavement Damage Identification Tool on Roads Based on Deep Learning and Surface Distress Index Efforts to Support Sustainable Transportation Infrastructure".
Arfian explained that the system is a tool designed to detect road pavement damage automatically using the YOLOv5s algorithm which is implemented in the Embedded Ai Kit and integrated withwebsiteas a medium of information. The system also adopts Adaptive RoI to increase the detection accuracy of five road pavement defects in the form of holes, crocodile skin cracks, longitudinal cracks, transverse cracks and ruts.
Next, after detecting the type of road damage, a process of estimating the area of road pavement damage will be carried out using the Area Estimation System which is implemented in the Embedded AI Kit and then sent to the server. Then the data is grouped into four categories using the integrated surface distress index methodwebsite.
“Our system is integrated with the pagewebsitewhich includes a mapping system display, analysis features, featuresuploadroad damage, and print report features. As forpin pointmappingwebsiteobtained using conversionreverse geocodingto change the location coordinates obtained by the GPS module.” Arfian explained.
In closing, Arfian said that the system was designed to help related agencies in terms of automatic and road damage surveysrealtime, supporting technological progress in the era of industrial revolution 4.0 andsociety5.0 in the transportation sector, as well as reducing the number of accidents. *(Siroga/PRIP PNM)
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