MS AFF: a novel semantic segmentation approach for buried object based on multi scale attentional feature fusion
Infrared technology is widely used in buried object detection since it can capture the heat radiated outward from the target objects. Compared to the visible images, infrared images present poor resolution, low contrast, and fuzzy visual effect, making it challenging to detect the target objects, specifically in complex backgrounds. In recent years, deep learning based methods have made significant improvements in detection tasks. However, infrared images of buried objects are difficult to obtain. Less training samples, worse performance of deep learning based detection. We raise a multi scale attentional feature fusion 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. Finally, we use state of the art object detection methods to train the buried object dataset and compare them with our proposed method. Extensive experiments on infrared segmentation dataset demonstrate the superiority of our method.