Prior-Information Auxiliary Module: An Injector to a Deep Learning Bridge Detection Model

Abstract

The lack of data samples has become a bottleneck for the performance improvement of bridge detection model training via transfer learning. This article proposes a deep learning bridge detection algorithm aided by a traditional bridge detection method to alleviate this problem. First, we explore the theoretical constraints on the integration of prior-information feature maps extracted via the traditional unsupervised method. Second, we design a prior-information auxiliary module (PAM) to receive and inject prior information. Finally, we introduce this module into different kinds of object detection networks for testing. The experimental results show that prior masks generated through multiextremal segmentation can assist object detectors in many respects. In addition, the PAM can use a few prior-information feature maps to enhance the performance of four representative object detection models on the bridge recognition dataset from the 2020 Gaofen challenge on automated high-resolution earth observation image interpretation. Our method preserves the validity of end-to-end training, making the traditional method more valuable in the artificial intelligence context.