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.

中文翻译：

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先验信息辅助模块：深度学习桥梁检测模型的注入器


数据样本的缺乏已经成为通过迁移学习训练桥梁检测模型性能提升的瓶颈。本文提出了一种在传统桥梁检测方法的辅助下的深度学习桥梁检测算法来缓解这一问题。首先，我们探讨了通过传统无监督方法提取的先验信息特征图整合的理论限制。其次，我们设计了一个先验信息辅助模块（PAM）来接收和注入先验信息。最后，我们将该模块引入不同类型的目标检测网络中进行测试。实验结果表明，通过多极值分割生成的先验掩模可以在许多方面为目标检测器提供帮助。此外，PAM 可以使用一些先验信息特征图来增强 2020 年高分自动高分辨率对地观测图像解译挑战赛桥梁识别数据集上四个代表性物体检测模型的性能。我们的方法保留了端到端训练的有效性，使传统方法在人工智能环境中更有价值。