Performance Evaluation of Contrast Enhancement Techniques for YOLOv8-Based Fall Detection under Low-Light Conditions

The detection of falls in low-light indoor environments is a challenge for vision-based systems because of the low visibility of features and degradation of contrast. Deep learning techniques with the YOLO algorithm provide an alternative to contact-based measurements but struggle under poor lighting conditions, and existing solutions are based on expensive IR or thermal cameras. In this study, a structured evaluation framework is proposed to systematically study the effect of contrast enhancement on fall detection with conventional RGB cameras in a controlled low-light indoor environment. Three enhancement techniques, namely Histogram Equalization (HE), Adaptive Equalization (AE), and Contrast Stretching (CS), are tested under various lighting conditions. The results show the consistent and systematic relationship between contrast restoration and the detection performance, with global enhancement showing more stable and precise results. Higher accuracy is obtained in darker scenes (75%) and dimly lit scenes (62.5%), with a precision of 1.000 in darker scenes with a high level of accuracy and thus reducing false alarms. The proposed framework can generate reproducible evidence for improving low-light detection performance and does not need any special imaging hardware or increase the complexity of models, which can promote the development of stable and scalable indoor fall detection systems for real applications.