RANCANG BANGUN OBJECT DETECTION PADA ROBOT SOCCER MENGGUNAKAN METODE SINGLE SHOT MULTIBOX DETECTOR (SSD MOBILENETV2) - Wahyu Pramana, Cokorda Gde; Care Khrisne, Duman; Putra Sastra, Nyoman (Jurnal SPEKTRUM; Vol 8 No 2 (2021): Jurnal SPEKTRUM, 2021)
Artificial intelligence or AI is a technology that emphasizes machine intelligence in responding like humans, developed to help support human work. AI has widely applied in various fields such as industry, medical, education, business, and robotics. The development of AI in the field of robotics produces autonomous robots, one example is the KRSBI-Beroda soccer robot. This research discusses the application of AI in the design of object detection systems using the single-shot multibox detector (SSD) model on the KRSBI-Beroda soccer robot. This study aims to produce an AI model in the form of an artificial neural network (ANN) implanted in the soccer robot to distinguish between ball, goal, robot, and obstacle objects. This object detection system was built using a deep learning method assisted by the TensorFlow Object Detection API framework using the MobileNetV2 SSD model which is run using the python programming language on the NVIDIA Jetson Nano board which is integrated into the C922 Pro webcam camera. The model was built using a dataset of 977 images consisting of 3064 objects that were trained as many as 200,000 steps on Google Colaboratory. The results showed a model with an average mAP of 0.80 with an average total loss of 1.5. Validation of the model resulted in a success rate of object prediction with an average accuracy of up to 98.45%.