Low Light Image Enhancement for Video Object Detection Using Modified Zero DCE Deep Learning Model

Abstract

This paper shows how poor lighting can severely affect movies and influence the performance of video object detection systems and their practical applicability. This research problem was solved with the help of the proposed image enhancement model aimed at increasing the visibility and quality of low-illumination images. Hence, this study aims to help improve video object detection, particularly in regions of low illumination, utilizing the Enhanced Zero DCE model. This deep-learning framework does not require any reference images; therefore, it is appropriate for real-time applications. Enhanced Zero DCE eliminates DCE on high-order tonal curves, whereas the deep neural network boosts pixel values, resulting in better quality images. Using a variety of loss functions, including color constancy, exposure matching, smoothness, and spatial consistency, the model was deployed in the LoL dataset, which includes both high-and low-illumination pictures. From the experimental findings, a drastic enhancement in image quality improvement performance was evident. In terms of numbers, the effectiveness of the proposed model is outlined as follows: there is a gain of 23 percent luminance, 17 percent average illumination, 20 percent of the histogram mean, and illumination on objects when compared to traditional methods. These improvements were evident in an increase in detection accuracy by 15% and precision by 20%. Therefore, it can be said that the integration of the advanced Enhanced Zero DCE model significantly increases the efficiency of video detection of objects under dim-light conditions. These enhancements have practical applications in surveillance, automobiles, and other real-time video monitoring, especially in situations where accurate detection of objects is paramount.