人工智能
计算机科学
计算机视觉
目标检测
视觉对象识别的认知神经科学
深度学习
卷积神经网络
模式识别(心理学)
Viola–Jones对象检测框架
光学(聚焦)
卷积(计算机科学)
对象(语法)
对象类检测
面部识别系统
人工神经网络
人脸检测
物理
光学
作者
Chun-Yi Lin,Muhamad Amirul Haq,Jiun-Han Chen,Shanq-Jang Ruan,Edwin Naroska
出处
期刊:IEEE Transactions on Circuits and Systems for Video Technology
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:: 1-1
标识
DOI:10.1109/tcsvt.2023.3292940
摘要
Recently, deep learning has been widely employed across various domains. The Convolution Neural Network (CNN), a popular deep learning algorithm, has been successfully utilized in object recognition tasks, such as face recognition, vehicle recognition, and license plate recognition. However, conventional methods for object recognition may not be appropriate for low-light image recognition due to information loss in the dark regions and unexpected noise that can impair object quality. Therefore, the development of specialized techniques for low-light image enhancement has become a major research focus for object detection. This paper proposed a gradient-based saliency map detection method with an improved ResNet architecture that outperforms previous works in detecting multiple or large objects. Additionally, the proposed method enhances images with the object as the center and emphasizes foreground-background differences. Compared with previous works, this paper achieves 1.28× improvements in the parameters and 1.32× faster inference speed than the original ResNet architecture.
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