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High-resolution (HR) imaging technology is increasingly employed to capture crack images in civil infrastructure, which is vital for ensuring the safety of the bridge inspection process conducted via unmanned aerial vehicles (UAVs). Such applications require the development of advanced algorithms for the segmentation of HR images. Traditional deep learning-based segmentation methods for inferencing HR images consume considerable GPU resources, which prompts the authors to draw inspiration from the cost-effective rendering technique in computer graphics and try to apply this advanced method to the refined segmentation of HR crack images. However, the original rendering method, designed to guide rendering points by the coarse segmentation masks, often inadequately directs rendering points towards the crucial boundary areas of tiny cracks, leading to unclear or missing boundary predictions. To address this, an innovative rendering technique was proposed, utilizing probability maps to precisely direct rendering points towards crack boundaries and tiny-crack branches during inference. This method enhances the accuracy of crack boundary segmentation and reduces the miss rate of tiny crack branches from HR images, all while conserving computational resources. Through model parameter experiments and ablation studies, the optimal model was obtained, and the effectiveness of the improved components was demonstrated. Furthermore, the field test has confirmed that, equipped with the proposed point rendering technique, the UAV is permitted to effectively perform crack inspection within a 3-m distance from the main beam. Compared to traditional low-resolution semantic segmentation methods, the UAV bridge inspection time is significantly reduced by 50% while maintaining the same accuracy.
Chu et al. (Thu,) studied this question.