In summary, the main contribution is three-fold: We propose an edge computing driven deep learning method for object detection in low-light conditions, which designs the overall structure of cloud-based enhancement and edge-based detection stages to keep balance between edge. In summary, the main contribution is three-fold: We propose an edge computing driven deep learning method for object detection in low-light conditions, which designs the overall structure of cloud-based enhancement and edge-based detection stages to keep balance between edge. Low-light image enhancement aims to improve the visibility of degraded images to better align with human visual perception. While diffusion-based methods have shown promising performance due to their strong generative capabilities. However, their unidirectional modelling of degradation often. This paper introduces an efficient and robust Frequency-Space Recovery Network (FSRNet), specifically designed for low-light image enhancement in architectural contexts, tailored to the unique characteristics of such scenes. The encoder utilizes a Feature Refinement Feedforward Network (FRFN) to. Considering both technologies for successful AI-based applications, we propose an edge-computing driven and end-to-end framework to perform tasks of image enhancement and object detection under low-light conditions. However, while improving image brightness, it is difficult to effectively maintain the texture and details of the image, and the quality of the image cannot be guaranteed. This research problem was solved with the help of the proposed image enhancement model aimed at increasing the visibility and. However, current image restoration methods predominantly focus on recovering images from RGB images, overlooking the potential of incorporating more modalities. With the advancements in per-sonal handheld devices, we can now easily capture images with depth information using devices such as mobile.
