Pengembangan Real-Time Object Detection System pada Perangkat Single-Board Computer - Riyadhul Fajri; Sriwinar; Firzha Fitria (KLIK: Kajian Ilmiah Informatika dan Komputer, 2023)
Technological developments in the field of artificial intelligence have opened the door to increasingly sophisticated applications in visual analysis and object detection. This research aims to implement a real-time object detection system using a Raspberry Pi device, a low-power Single Board Computer (SBC) based device with limited hardware resources. We used TensorFlow Lite, a lightweight version of the TensorFlow deep learning framework, to run object detection models on a Raspberry Pi. Our system is powered by a USB web cam as an image source. The research results show that the Raspberry Pi is able to carry out real-time object detection well, with adequate resource usage efficiency. The system built is able to perform better classification, which shows a better detection speed from using TensorFlow Lite, compared to using TensorFlow. Although there are constraints in terms of detection speed depending on the complexity of the model and the number of objects, these results open up great opportunities for the use of embedded devices in various applications such as security monitoring and image analysis. This study also emphasizes the importance of model optimization to achieve the best performance on low-power devices