International Journal of Science, Engineering and TechnologyAuthors: Arjit Sasan, Abhishek Bhardwaj, Dev Karan Singh, Gurdeep Singh Panwar
Abstract: Low-light conditions present significant obstacles in the design of computer vision solutions, considering that dark images have poor visibility, reduced contrast, unseen details, and higher noise levels, which result in reduced object detection system performance. This research proposes Lumnia, a low-light image enhancement and object detection solution that utilizes multiple image enhancement algorithms and YOLOv8 object detection. The system employs four enhancement modes, which include CLAHE (Contrast Limited Adaptive Histogram Equalization), Gamma Correction, simplified Zero-DCE inspired image enhancement technique, and Auto Enhancement mode. The enhanced image is then sent for further processing using YOLOv8 to detect objects. In addition, the solution uses several image quality metrics, such as MSE (Mean Squared Error), PSNR (Peak Signalto-Noise Ratio), and SSIM (Structural Similarity Index) to evaluate the image enhancement process and assess the differences between the original and enhanced images. The project was written using Python programming language, and it makes use of Flask web framework, OpenCV library, NumPy package, scikit-image, and Ultralytics YOLOv8. Experiments reveal that various image enhancement methods result in varied image visibility and object detection results.
