OBJECT DETECTION FOR LOW-LIGHT ENVIRONMENT USING MULTISCALE RETINEX - Anthonius Adi Nugroho (Jurnal Riset Informatika, 2026)
Object detection is a critical task in computer vision, yet its performance degrades significantly under low-light conditions due to loss of detail and diminished features. This study proposes an image enhancement framework to improve detection robustness in challenging lighting. The methodology integrates Multiscale Retinex (MSR) for image enhancement and SSD MobileNet V2 for object detection. MSR was configured with optimal parameters (scale1:10, scale2:60, scale3:180, σ:100, β:30) to enhance brightness while preserving crucial image details. The experimental results demonstrate that Retinex correction is highly effective in extreme low-light scenarios. In 0 lux conditions, where objects were completely undetectable without processing, the proposed method enabled detection with confidence levels between 62% and 96%, yielding an average accuracy increase of 50%. In 15 lux conditions, accuracy improved by 6.6%. However, the system degraded at intensities above 25 lux, suggesting that the enhancement is most beneficial in near-dark environments. In conclusion, Multiscale Retinex significantly enhances the capability of SSD MobileNet V2 for object detection in environments with illumination below 77 lux. This approach provides a viable solution for improving the reliability of surveillance and automated systems operating in unpredictable lighting.