Vehicle and drone-mounted surveillance equipment face severe computational constraints, posing significant challenges for real-time, accurate crack segmentation. This paper introduces the crack location segmentation transformer (CLST) to address these issues. Images are processed to better resemble patches associated with cracks, enabling precise segmentation while significantly reducing the model’s computational load. To handle varying segmentation challenges, a range of models with different computational demands has been designed to suit diverse needs. The most lightweight model can be deployed for real-time use on edge devices. A module in the neck of the pipeline encodes crack coordinate information, and end-to-end training has resulted in state-of-the-art performance across multiple datasets.

中文翻译：

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使用可拆分变压器和位置坐标的裂纹实例分割


车载和无人机安装的监控设备面临严重的计算限制，对实时、准确的裂纹分割提出了重大挑战。本文介绍了裂缝位置分割变换器 （CLST） 来解决这些问题。图像经过处理后，可以更好地与裂纹相关的补丁，从而实现精确分割，同时显著降低模型的计算负载。为了应对不同的分割挑战，我们设计了一系列具有不同计算需求的模型来满足不同的需求。最轻量级的模型可以部署在边缘设备上实时使用。管道颈部的模块对裂纹坐标信息进行编码，端到端训练在多个数据集中实现了最先进的性能。