Implementation of Convolutional Neural Network Algorithm for Detecting Empty Parking Area Based on Raspberry Pi - Suhartono; Satria Gunawan Zain; Sadaruddin, Ayu Futri (Politeknik Piksi Ganesha Indonesia, 2024)
This study aims to implement a convolution neural network algorithm to detect empty parking areas based on Raspberry Pi 4 and use the Convolutional Neural Network (CNN) method of the YOLO V5 model. This research consists of several stages, starting from the potential and problem stages, needs analysis, literacy studies, building prototypes, system design, and system testing. The datasets collected were taken using smartphone cameras and webcams with a total of 645 image datasets which were divided into two categories, namely training data and validation. System testing is carried out in two conditions, namely during the day and at night. The results of the detection test for observing variations in the position of filled and unfilled vehicles obtained the highest average accuracy during daytime conditions, while for observing cars entering and leaving the parking lot during day and night conditions, the results were the same percentage of success.