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Vol. 11, No. 4, November 2026 (Article in Progress)

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Evaluating CLAHE and Temporal Smoothing Impact on Deep Learning-Based Young Crescent Moon Video Detection

https://doi.org/10.22219/kinetik.v11i4.2717
Bayu Krisna Murti
Universitas Ahmad Dahlan
Kartika Firdausy
Universitas Ahmad Dahlan
Murinto
Universitas Ahmad Dahlan

Corresponding Author(s) : Bayu Krisna Murti

2408048018@webmail.uad.ac.id

Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, Vol. 11, No. 4, November 2026 (Article in Progress)

Abstract

Detecting the young crescent moon from video data poses significant challenges due to low contrast against the twilight sky, sensor noise, and atmospheric interference. Frame-wise contrast enhancement, such as Contrast Limited Adaptive Histogram Equalization (CLAHE), can introduce inter-frame intensity fluctuations that degrade both visual quality and the consistency of automated detection. This study evaluates the impact of CLAHE and Temporally Aware CLAHE (TA-CLAHE) preprocessing on YOLOv8n-based young crescent moon detection across three video sequences representing varying observational conditions. Temporal stability was assessed using Flicker Index (FI), Percent Flicker (PF), Temporal Standard Deviation (T-STD), and Frame Difference Mean (FDM). At the same time, detection performance was measured using Detection Consistency Rate (DCR) and centroid stability relative to a fixed ground truth. CLAHE substantially improves detection consistency, raising DCR by up to 16 percentage points over the Raw baseline (Video 1: 60.8% to 76.8%). TA-CLAHE further elevates detection to near-complete coverage — 99.9% on Video 1 and 99.2% on Video 3 — surpassing standard CLAHE by 23.1 and 9.5 percentage points, respectively. Temporal stability also improves: TA-CLAHE reduces FI by 19–32% and T-STD by 20–26%; the reduction in PF is modest (at most 27%) and is retained only as a secondary indicator given its known measurement limitations. These gains involve trade-offs: enhancement slightly loosens per-box localization, and under cloud occlusion, TA-CLAHE raises the invalid detection rate to 17%. Based on these findings, CLAHE is recommended as a sound, low-cost default preprocessing, while TA-CLAHE is preferred for applications requiring an uninterrupted, low-jitter detection track, provided it is paired with an explicit occlusion gate.

Keywords

Young Crescent Moon Detection CLAHE Temporal Smoothing YOLOv8 Video Processing Flicker Reduction
Krisna Murti, B., Firdausy, K., & Murinto. (2026). Evaluating CLAHE and Temporal Smoothing Impact on Deep Learning-Based Young Crescent Moon Video Detection . Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control, 11(4). https://doi.org/10.22219/kinetik.v11i4.2717
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