A Self-Construction of Automatic Crescent Detection Using Haar-Cascade Classifier and Support Vector Machine

Abstract

Developing an automatic detection method based on computer vision applied to the moon crescent is an innovative concept that can be further developed. This program will be highly useful for observers during the Moon crescent observation because it can help them recognize objects quickly. This paper proposes an automatic crescent moon detection method based on visual mechanisms and training using the Cascade Classifier algorithm. The stages of this method consist of building Haar structural features, extracting feature samples using Haar structural features, and training 981 images consisting of 654 positive images and 327 negative images using the Cascade Classifier. The results show that the crescent moon detection performance is quite good at detecting the crescent Moon. The developed program can recognize crescent moon objects, although it is limited to relatively large lunar illumination in the range of greater than 10% to less than 50%. Furthermore, our program can be applied in real-time situations.