OBJECT CLASSIFICATION BASED ON THERMAL IMAGES USING DEEP LEARNING (PRE-TRAINED RESNET 50)
Abstract
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.
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