GHE-Ensemble: Enhanced Hybrid Image Enhancement Model for Night Vision Multi-Object detection in Autonomous Vehicle

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Ranjitha P, Saira Banu Atham


Autonomous vehicle is the exponential growing topic in the research filed due its huge demand in the current industry. Where object detection plays the major role to take any major decision. Object detection in the normal vision yields the excellent performance due to deep learning models. But object detection in the night vision does not yield the good performance due to various challenges in it. In the proposed work created a night vision database called BDD-Darko where it contains night vision images with multi objects. Applied novel GHE-Ensemble model for image enhancement technique and trained using deep learning model Yolov5 for the multi-object detection in the night vision. Which resulted in 65.3 % accuracy. Proposed model is yielding better results for image enhancement technique for night vision than the previous existing model and detecting the multi object in the single frame with better performance. This paper proposes

(1)A new database called as BDD-Darko which contains night vision image with multiple objects. (2) A Novel GHE-Ensemble model for image enhancement technique for night vision images

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