A novel low light object detection method based on the YOLOv5 fusion feature enhancement

• Published in: Scientific reports Vol. 14; no. 1; pp. 4486 - 15
• Main Authors: Peng, Daxin, Ding, Wei, Zhen, Tong
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 23.02.2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/987; 639/624/1075/1083; Accuracy; Algorithms; Computer vision; Datasets; Deep learning; Humanities and Social Sciences; Image enhancement; Light; Low-light scene; multidisciplinary; Noise reduction; Object detection; Receptive field; Science; Science (multidisciplinary); Deep learning; Image enhancement; Low-light scene; Object detection

Low-light object detection is an important research area in computer vision, but it is also a difficult issue. This research offers a low-light target detection network, NLE-YOLO, based on YOLOV5, to address the issues of insufficient illumination and noise interference experienced by target detecti...