SEGMENTASI OBJEK BERBASIS GAMBAR TERMAL MENGGUNAKAN DEEP LEARNING (PRE-TRAINED RESNEXT 50) - Fauzan, R. Aldam Dwi; Satyawan, Arief Suryadi; Siswanti, Sri Desy; Puspita, Heni (Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (SENASTINDO), 2022)
The transportation sector at this time has experienced many technological developments which have been well receifed by the public, especially the people of Indonesia. Along with the development of transportation technology has undergone many developments, with sophistication and increased comfort and better security. So Autonomus Car technology was created that can help drivers to maintain safety while driving. Autonomus car was built using the Neural Network control method, and also Image Processing as signal processing with image input, and with a flip camera used for vehicle input data. Autonomous cars have many positive impacts on human life today, so humans can minimize time properly. Travel safety is maintained, and can be more productive when driving. The method that is currently developing rapidly is automatic extraction using deep learning. In this final project, automatic extraction method with deep learning technology used is Fully Convolutional Network (FCN) with Residual Neural Network Next (ResNext) architecture. In this study, the extraction accuracy for automatic vehicle function training reached 98% for ResNext 50 with a resolution of 640x540 pixels. Semantic segmentation will then test with 34030 image frames offline. In ResNext 50 architecture contains 20512 frames in good category, 7883 in adequate category and 5605 in poor category.