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首页> 外文期刊>Journal of advanced transportation >CNN-Enabled Visibility Enhancement Framework for Vessel Detection under Haze Environment
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CNN-Enabled Visibility Enhancement Framework for Vessel Detection under Haze Environment

机译：Haze环境下支持CNN的可见性增强框架，用于阴霾环境下的船舶检测

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Maritime images captured under haze environment often have a terrible visual effect, making it easy to overlook important information. To avoid the failure of vessel detection caused by fog, it is necessary to preprocess the collected hazy images for recovering vital information. In this paper, a novel CNN-enabled visibility dehazing framework is proposed, consisting of two subnetworks, that is, Coarse Feature Extraction Module (C-FEM) and Fine Feature Fusion Module (F-FFM). Specifically, C-FEM is a multiscale haze feature extraction network, which can learn information from three scales. Correspondingly, F-FFM is an improved encoder-decoder network to fuse multiscale information obtained by C-FEM and enhance the visual effect of the final output. Meanwhile, a hybrid loss function is designed for monitoring the multiscale output of C-FEM and the final result of F-FFM simultaneously. It is worth mentioning that massive maritime images are considered the training dataset to further adapt the vessel detection task under haze environment. Comprehensive experiments on synthetic and realistic images have verified the superior effectiveness and robustness of our CNN-enabled visibility dehazing framework compared to several state-of-the-art methods. Our method preprocesses images before vessel detection to demonstrate our framework has the capacity of promoting maritime video surveillance.
机译：在阴霾环境下捕获的海事映像通常具有可怕的视觉效果，使得容易忽视重要信息。为了避免雾引起的血管检测失败，必须预处理收集的朦胧图像以恢复重要信息。在本文中，提出了一种新的CNN的可视性消化框架，由两个子网组成，即粗糙度特征提取模块（C-FEM）和微观特征融合模块（F-FFM）。具体而言，C-FEM是多尺度阴霾特征提取网络，其可以从三个尺度学习信息。相应地，F-FFM是一种改进的编码器 - 解码器网络，用于熔断由C-FEM获得的多尺度信息，并增强最终输出的视觉效果。同时，混合损耗功能专为监测C-FEM的多尺度输出和同时F-FFM的最终结果。值得一提的是，巨大的海上图像被认为是训练数据集，以进一步调整雾化环境下的血管检测任务。与多种最先进的方法相比，综合性和现实图像的综合实验已经验证了能够的CNN的可见度除虫框架的卓越效力和稳健性。我们的方法在船舶检测之前预处理图像，以展示我们的框架具有促进海上视频监控的能力。

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