Volume 20 No 22 (2022)
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Detecting Moving Objects in Dense Fog Environment using Fog-Aware-Detection Algorithm and YOLO
Sharmistha Puhan, Sambit Kumar Mishra
This paper proposes a novel algorithm, called Fog-Aware-Detection, for detecting moving objects in dense fog environments. The algorithm leverages the characteristics of fog to enhance detection performance by extracting foreground objects from the foggy background. The algorithm involves two main stages: a pre-processing stage for enhancing the foggy image and a detection stage for extracting moving objects from the pre-processed image. The pre-processing stage employs a fog removal technique and a contrast enhancement method to reduce the effect of fog and improve the visibility of objects. The detection stage uses a background subtraction technique to detect moving objects in the pre-processed image. The proposed algorithm is evaluated on a publicly available foggy dataset and achieves promising results in terms of detection accuracy and robustness to various fog densities. The proposed algorithm can be useful for applications such as autonomous driving, surveillance, and navigation systems in foggy environments.
Foggy weather, Object detection, YOLO, De-hazing, Defogging.
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