DEVELOPMENT OF A GENETIC ALGORITHM FOR OPTIMIZING CONVOLUTIONAL NEURAL NETWORKS IN ORDER TO IMPROVE THE ACCURACY OF OBJECT DETECTION IN DIFFICULT LIGHTING AND BACKGROUND CONDITIONS

This article addresses the challenge of improving object detection accuracy in video data captured under low-light conditions. Modern video detection systems—particularly in areas such as security, autonomous systems, and medicine—often suffer from reduced accuracy due to poor lighting. The proposed method is based on the integration of the YOLOv5 object detection model with a variety of image processing filters (including CLAHE, gamma correction, histogram equalization, Gaussian blur, bilateral filtering, the Non-Local Means algorithm, Gray-World and Max-RGB balancing schemes, as well as Retinex and MSRCR methods) and genetic algorithms. This approach enhances both the reliability of detection and computational efficiency. Experimental evaluations demonstrate that the proposed system achieves significantly higher object detection accuracy in low-light data compared to traditional methods.