Mr. Yuxiang Cao Profile

Yuxiang Cao

at Fudan Univ

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Proceedings Article | 15 December 2022 Paper

Image segmentation for night-vision surveillance camera based on deep learning

Yuxiang Cao, Junhua Wang, Lizhe Qi, Min Xu

Proceedings Volume 12478, 1247836 (2022) https://doi.org/10.1117/12.2654811

KEYWORDS: Image segmentation, Surveillance, Cameras, Imaging systems, Convolution, Image processing, Low light level imaging

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Under low illumination environments, the insufficient visible light and the existence of near-infrared light will cause photon noise and color distortion for the imaging of night-vision CMOS sensor. The light source highly affects the imaging of surveillance camera and declines the accuracy of semantic segmentation. In this work, we report a modified convolutional neural network based on DeepLabV3+. We modify the backbone of the network from Xception to MobileNetV2 to deal with the real-time vision task of night-vision surveillance camera. Linear bottleneck and inverted residuals are adopted in MobileNetV2, and they greatly reduce parameters of the network. A real-world low-light dataset with fine annotations for night-vision surveillance camera is proposed to train and evaluated the new framework. Aiming at the problem of insufficient training samples, transfer learning and a new image enhancement strategy are carried out to complete the training. We also change the loss function to a joint loss function to further improve the results of segmentation. Comparing with other existing state-of-the-art algorithms, the modified neural network shows competitive performance on both subjective and objective assessments. The ablation study comparing with the baseline model proves the effectiveness of the modifications.

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