MS-AFF: a novel semantic segmentation approach for buried object based on multi-scale attentional feature fusion

Abstract

Infrared technology is a widely used in precision guidance and mine detection since it can capture the heat radiated outward from the target object. We use infrared (IR) thermography to get the infrared image of the buried obje cts. Compared to the visible images, infrared images present poor resolution, low contrast, and fuzzy visual effect, which make it difficult to segment the target object, specifically in the complex backgrounds. In this condition, traditional segmentation methods cannot perform well in infrared images since they are easily disturbed by the noise and non-target objects in the images. With the advance of deep convolutional neural network (CNN), the deep learningbased methods have made significant improvements in semantic segmentation task. However, few of them research Infrared image semantic segmentation, which is a more challenging scenario compared to visible images. Moreover, the lack of an Infrared image dataset is also a problem for current methods based on deep learning. We raise a multiscale attentional feature fusion (MS-AFF) module for infrared image semantic segmentation to solve this problem. Precisely, we integrate a series of feature maps from different levels by an atrous spatial pyramid structure. In this way, the model can obtain rich representation ability on the infrared images. Besides, a global spatial information attention module is employed to let the model focus on the target region and reduce disturbance in infrared images' background. In addition, we propose an infrared segmentation dataset based on the infrared thermal imaging system. Extensive experiments conducted in the infrared image segmentation dataset show the superiority of our method.

Study on the 3D point cloud semantic segmentation method of fusion semantic edge detection

“…The edge contour features with semantic information are obtained from the above network structure. In the process of two-way feature fusion, two-way information is used by weighted fusion [15], where the global semantic feature is 𝐴 and the semantic edge feature is 𝐵 . The fused feature 𝑀 can be expressed as:…”