Lightweight defocus deblurring network for curved-tunnel line scanning using wide-angle lenses

IF 8.2 1区 工程技术 Q1 ENGINEERING, CIVIL Underground Space Pub Date : 2024-09-13 DOI:10.1016/j.undsp.2024.06.005
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Abstract

High-resolution line scan cameras with wide-angle lenses are highly accurate and efficient for tunnel detection. However, due to the curvature of the tunnel, there are variations in object distance that exceed the depth of field of the lens, resulting in uneven defocus blur in the captured images. This can significantly affect the accuracy of defect recognition. While existing deblurring algorithms can improve image quality, they often prioritize results over inference time, which is not ideal for high-speed tunnel image acquisition. To address this issue, we developed a lightweight tunnel structure defect deblurring network (TSDDNet) for curved-tunnel line scanning with wide-angle lenses. Our method employs an innovative progressive structure that balances network depth and feature breadth to simultaneously achieve good performance and short inference time. The proposed depthwise ResBlocks significantly improves the parameter efficiency of the network. Additionally, the proposed feature refinement block captures the structurally similar features to enhance the image details, increasing the peak signal-to-noise ratio (PSNR). A raw dataset containing tunnel blur images was created using a high-resolution line scan camera and used to train and test our model. TSDDNet achieved a PSNR of 26.82 dB and a structural similarity index measure of 0.888, while using one-third of the parameters of comparable alternatives. Moreover, our method exhibited a higher computational speed than that of conventional methods, with inference times of 8.82 ms for a single 512 × 512 pixels image patch and 227.22 ms for completely processing a 2048 × 2560 pixels image. The test results indicated that the structural scalability of the network allows it to accommodate large inputs, making it effective for high-resolution images.
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使用广角镜头进行曲线隧道线扫描的轻量级去焦模糊网络
配备广角镜头的高分辨率线扫描相机在隧道探测方面具有高精确度和高效率。然而,由于隧道的弧度,物体距离的变化会超过镜头的景深,从而导致捕捉到的图像出现不均匀的虚焦模糊。这会严重影响缺陷识别的准确性。虽然现有的去模糊算法可以提高图像质量,但它们通常会优先考虑结果而不是推理时间,这对于高速隧道图像采集来说并不理想。为了解决这个问题,我们开发了一种轻型隧道结构缺陷去毛刺网络(TSDDNet),用于使用广角镜头进行曲线隧道线扫描。我们的方法采用了创新的渐进式结构,在网络深度和特征广度之间取得了平衡,从而同时实现了良好的性能和较短的推理时间。所提出的深度ResBlocks大大提高了网络的参数效率。此外,提出的特征细化块能捕捉结构相似的特征,从而增强图像细节,提高峰值信噪比(PSNR)。我们使用高分辨率线扫描相机创建了一个包含隧道模糊图像的原始数据集,用于训练和测试我们的模型。TSDDNet 的 PSNR 为 26.82 dB,结构相似度指数为 0.888,而使用的参数仅为同类替代方法的三分之一。此外,与传统方法相比,我们的方法具有更高的计算速度,单个 512 × 512 像素图像片段的推理时间为 8.82 毫秒,完全处理一幅 2048 × 2560 像素图像的推理时间为 227.22 毫秒。测试结果表明,该网络的结构可扩展性使其能够适应大型输入,从而有效地处理高分辨率图像。
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来源期刊
Underground Space
Underground Space ENGINEERING, CIVIL-
CiteScore
10.20
自引率
14.10%
发文量
71
审稿时长
63 days
期刊介绍: Underground Space is an open access international journal without article processing charges (APC) committed to serving as a scientific forum for researchers and practitioners in the field of underground engineering. The journal welcomes manuscripts that deal with original theories, methods, technologies, and important applications throughout the life-cycle of underground projects, including planning, design, operation and maintenance, disaster prevention, and demolition. The journal is particularly interested in manuscripts related to the latest development of smart underground engineering from the perspectives of resilience, resources saving, environmental friendliness, humanity, and artificial intelligence. The manuscripts are expected to have significant innovation and potential impact in the field of underground engineering, and should have clear association with or application in underground projects.
期刊最新文献
Detecting soil mixing, grain size distribution, and clogging potential of tunnel excavation face by classification-regression algorithms using EPBM operational data Experimental study on mechanical behavior and countermeasures of mountain tunnels under strike-slip fault movement RM2D: An automated and robust laser-based framework for mobile tunnel deformation detection Lightweight defocus deblurring network for curved-tunnel line scanning using wide-angle lenses Grain-based coupled thermo-mechanical modeling for stressed heterogeneous granite under thermal shock
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