计算机科学 ›› 2021, Vol. 48 ›› Issue (2): 142-147.doi: 10.11896/jsjkx.200500158
詹瑞, 雷印杰, 陈训敏, 叶书函
ZHAN Rui, LEI Yin-jie, CHEN Xun-min, YE Shu-han
摘要: 街景变化检测对于自然灾害破坏和城市发展变化的研究起着重要作用。其主要目标是将成对的输入图片中变化的区域标注出来,其实质是二分类的语义分割问题。不同时间拍摄的街景图片可能受到如光线、天气、背景噪声、视角误差等诸多干扰因素的影响,这给传统的变化检测方法带来挑战。针对该问题,提出了一种新的神经网络模型(Multiple Difference Features Network,MDFNet)。该模型首先使用孪生网络提取成对输入图片的不同深度特征,并使用差异模块对相同深度特征计算差异,以此有效获得不同尺度的变化信息;然后通过JPU模块融合多重差异特征,在不损失细节信息的情况下提取其深层语义信息;最后使用金字塔池化模块结合全局和局部信息生成二分类的变化检测图像。在PCD数据集上的GSV和TSUNAMI部分分别采用5折交叉验证法对模型进行实验,实验结果表明,MDFNet获得了0.787和0.862的F-score,相比排名第二的DOF-CDNet方法,其值提高了约11.9%和2.9%,同时其能够更精准地分割变化细节。因此,所提模型可以有效应对干扰,对于复杂场景也具备优秀的检测能力。
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