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首页> 外文期刊>Journal of visual communication & image representation >Illumination-based adaptive saliency detection network through fusion of multi-source features
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Illumination-based adaptive saliency detection network through fusion of multi-source features

机译：通过多源特征融合的照明 - 基于自适应效力检测网络

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摘要

Salient object detection (SOD) tasks aim to outline the most concerned part of human vision, which is widely used in computer vision fields. Due to possibility of the insufficient illumination in the application environment (such as night or dim indoor environment), RGB images from visible channels usually lose most of their performance, while thermal images can improve the detection performance. Therefore, it is in urgent need of a robust saliency detection method, which can handle complex illumination conditions and take use of features from multiple sources intelligently. Accordingly, we propose our 'illumination based multi-source fused salient object detection network' (IAN-MF-SOD network). Taking the illumination condition as a quantitative reference, we guide features from two sources to fuse adaptively and intelligently, so that our method can enhance both of their advantages. For different illumination conditions, we distribute different fusion weights for each RGB-thermal image pair. Well fused images are generated as inputs to a trained SOD network to obtain saliency maps. Due to the analysis of our proposed IAN-score, our method performs favorably against traditional RGB-based SOD networks.
机译：突出的物体检测（SOD）任务旨在概述人类视力最有关部分，广泛应用于计算机视觉领域。由于应用环境中不足（如夜间或暗淡环境）的可能性，来自可见通道的RGB图像通常丢失其大部分性能，而热图像可以提高检测性能。因此，迫切需要坚固的耐药性检测方法，可以处理复杂的照明条件并智能地利用来自多个来源的特征。因此，我们提出了我们的“基于照明的多源融合对象检测网络”（IAN-MF-SOD网络）。将照明条件作为定量参考，我们引导两个来源的功能适应和智能地保险，因此我们的方法可以增强它们的两个优点。对于不同的照明条件，我们为每个RGB热图像对分发不同的熔合重量。融合图像被生成为培训的SOD网络的输入，以获得显着图。由于我们提出的IAN评分的分析，我们的方法对传统的RGB为基础的SOD网络表现有利。

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