Vacant parking space identification using probabilistic neural network

Romi Fadillah Rahmat, Sarah Purnamawati, Joko Kurnianto, Sharfina Faza, Muhammad Fermi Pasha


The need for public parking space is increasing nowadays due to the high number of cars available.  Users of car parking services, in general, are still looking for vacant parking locations to park their vehicle manually. With the current technological developments, especially in image processing field, it is expected to solve the parking space problem. Therefore, this research implements image processing to determine the location of vacant parking space or occupied ones that run in real-time. In this study, the proposed method is divided into five stages. The first stage is image acquisition to capture the image of parking location. Then it continues to pre-processing stage which consists of the process of saturation, grayscale and thresholding. The third stage is image segmentation to cut the image into five parts. The next stage is feature extraction using invariant moment, and the last stage would be identification process to determine the location of vacant parking spaces or occupied ones. The results of this research using 100 test images generates an accuracy, recall, and precision of 94%.


Image processing, Parking location, Probabilistic neural network, Identification, Invariant moment

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