KLASIFIKASI OBJEK BERBASIS GAMBAR THERMAL MENGGUNAKAN DEEP LEARNING (PRE-TRAINED RESNET 50) - Nugroho, Agung; Satyawan, Arief Suryadi; Siswanti, Sri Desy (Prosiding Seminar Nasional Sains Teknologi dan Inovasi Indonesia (SENASTINDO), 2022)
The development of technology in the field of transportation is currently something that is very enthusiastically welcomed by the Indonesian people in general. But along with the development of transportation technology that exists today is much different from the past, with a lot of sophistication and improving quality and safety that is more innovative. An Autonomous Car was created that can make it easier for drivers and maintain safety while driving. This system was built using the Neural Network control method, as well as Image Processing as the input signal in the form of images, and with the Flir camera as the vehicle data input. This of course has a very positive impact on human life today, of course humans will be more efficient in time, maintain safety on the trip, and can be more productive when driving. The method that is currently developing rapidly is automatic extraction using deep learning technology. The method used is Fully Convolutional Network (FCN) with Residual Neural Network (ResNet) architecture.The method currently used in the research is automatic extraction using deep learning technology to detect objects in the classification that has been made, with Residual Neural Network 50 (ResNet) architecture. In this study, the extraction accuracy for automatic vehicle function training reached 97.1% for ResNet 50 and 96.7% for ResNet 101 with a resolution of 640x512 pixels.