The Effect of Gamma Correction on the Accuracy of Vehicle Detection Using the YOLOv8 Algorithm

Accurate vehicle detection under low-light conditions is a significant challenge in traffic surveillance systems and computer vision applications. Although YOLOv8 performs well under normal illumination, its accuracy decreases when processing low-light images due to reduced contrast and limited visual details. This study proposes the integration of gamma correction as a preprocessing method to enhance image brightness and improve YOLOv8 detection performance. The dataset consists of real ATCS traffic camera recordings from Medan City under varying lighting conditions. Gamma correction with three values (0.5, 1.5, and 2.0) was applied to evaluate its effect on detection accuracy. The results show that gamma 1.5 provides the best improvement, increasing [email protected] by 0.14% and [email protected]:0.95 by 0.74%, and achieving the highest confidence score of 0.9678 while also producing more stable training convergence. The novelty of this study lies in applying gamma correction to YOLOv8 using real-world ATCS low-light data, demonstrating that simple preprocessing can enhance detection robustness without modifying the model architecture.