Edge Detection and Post-Edge Filtering for Crescent Detection Using Circular Hough Transform - Ady Suprayitno; Murinto; Kartika Firdausy (Journal of Innovation Information Technology and Application (JINITA), 2026)
Crescent detection based on digital images faces major challenges due to low contrast, atmospheric noise, and the very thin curved structure of the crescent moon. This study aims to evaluate the influence of edge detection and post-edge filtering methods on the performance of crescent moon detection based on the Circular Hough Transform (CHT). Three edge detection methods, namely Sobel, Canny, and Phase Stretch Transform (PST), were combined with three filtering techniques, namely Gaussian, Median, and Bilateral filtering. All images were processed using a fixed pre-processing pipeline consisting of image resizing, conversion to grayscale, and contrast enhancement using CLAHE. Performance evaluation was conducted using Accuracy, Precision, Recall, and F1-Score as the main metrics, and further verified through visual analysis of CHT detection results. The results show that Canny achieves the best performance in unfiltered conditions, while filtering significantly improves detection reliability depending on the characteristics of the edge detector. The combination of Canny and Bilateral filtering produces the highest accuracy and results in the most geometrically consistent CHT detection. On the other hand, Median filtering was found to be unsuitable for Canny due to its tendency to remove thin edge structures that are critical for crescent detection. These findings highlight the crucial role of edge map quality in CHT-based crescent detection and provide methodological guidance for selecting reliable combinations of edge detection and filtering in automated crescent observation systems.