Adaptive image enhancement technology based on bad weather
Jiaxiu Chang, Wenshuai Hou, Wenjing Chen, Jun Yan, Kaige Cui · 2024
To tackle the formidable challenges that adverse weather conditions pose for image object detection, this paper presents an innovative approach grounded in the Image Adaptive YOLO (IA-YOLO) framework. The framework has introduced a series of advanced strategies to address the challenges to the accuracy and reliability of object recognition under extreme weather conditions. In environments where visibility is reduced due to factors like rain, fog, or low light, traditional object detection methods often struggle to achieve satisfactory results. However, IA-YOLO aims to overcome these limitations by incorporating adaptive image enhancement techniques that can effectively improve the quality of captured images. By embedding a unique image refinement mechanism within an efficient convolutional neural network, IA-YOLO empowers the system to autonomously acquire superior parameters for image refinement through a minimally supervised learning approach. This approach ensures that the images are enhanced in a way that is specifically tailored to improve object detection performance. To encapsulate, the article presents IA-YOLO as an influential instrument for tackling the obstacles posed by inclement weather in the realm of image object detection. By leveraging adaptive image enhancement techniques, IA-YOLO aims to provide more accurate and reliable detections, even in the most challenging weather scenarios.