LAUSR

Jet fuel leaks not only waste resources and increase costs but also pose a risk of emergency landings and aviation accidents. With the blossom and implementation of deep learning, crack segmentation techniques have been rapidly developed in many fields. However, it is struggle to get accurate and complete crack segmentation results because of images’ complex background environment. To address this issue, we collected and labeled a dataset of 2824 crack images from the surface of aircraft fuel tank, named CAFT2800. And this article presents an atrous spatial pyramid fusion (ASPF) and hybrid attention network based on deep learning to deal with the complicated environment. The backbone of network uses a hierarchical structure Swin transformer to extract features. In the neck of the network, an ASPF module is proposed to further capture contextual information in multiple scale. Unlike the atrous spatial pyramid pooling (ASPP) module, which aggregates image-level information with pooling, we abandon this operation according to the characteristics of crack. And the neck structure is designed for fusing high-level semantic information to all scales. At the gate between the neck and the decoder head, a selective kernel (SK) block is embedded into the network to recalibrates channel-wise feature responses. Due to the morphological characteristics of crack, we propose an evaluation index, Thinning F1 score (TFscore), which is more meaningful compared to the commonly used F1 score. Sufficient control experiments were conducted on the CAFT2800 dataset and two complicated environment benchmarks (DeepCrack and GAPs) to test the effectiveness of the network, and our method achieved superior performance. Source code and the CAFT2800 are available at https://github.com/Gu-EH/CAFT2800.