A novel real-time pixel-level road crack segmentation network

IF 2.9 4区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Rongdi Wang, Hao Wang, Zhenhao He, Jianchao Zhu, Haiqiang Zuo
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引用次数: 0

Abstract

Road crack detection plays a vital role in preserving the life of roads and ensuring driver safety. Traditional methods relying on manual observation have limitations in terms of subjectivity and inefficiency in quantifying damage. In recent years, advances in deep learning techniques have held promise for automated crack detection, but challenges, such as low contrast, small datasets, and inaccurate localization, remain. In this paper, we propose a deep learning-based pixel-level road crack segmentation network that achieves excellent performance on multiple datasets. In order to enrich the receptive fields of conventional convolutional modules, we design a residual asymmetric convolutional module for feature extraction. In addition to this, a multiple receptive field cascade module and a feature fusion module with non-local attention are proposed. Our network demonstrates superior accuracy and inference speed, achieving 55.60%, 59.01%, 75.65%, and 57.95% IoU on the CrackForest, CrackTree, CDD, and Crack500 datasets, respectively. It also has the ability to process 143 images per second. Experimental results and analysis validate the effectiveness of our approach. This work contributes to the advancement of road crack detection, providing a valuable tool for road maintenance and safety improvement.

Abstract Image

新型实时像素级路面裂缝分割网络
道路裂缝检测在保护道路寿命和确保驾驶员安全方面起着至关重要的作用。依靠人工观测的传统方法在量化损坏方面存在主观性和低效率的局限性。近年来,深度学习技术的进步为裂缝自动检测带来了希望,但低对比度、小数据集和定位不准确等挑战依然存在。在本文中,我们提出了一种基于深度学习的像素级道路裂缝分割网络,该网络在多个数据集上都取得了优异的性能。为了丰富传统卷积模块的感受野,我们设计了一个用于特征提取的残差非对称卷积模块。除此之外,我们还提出了一个多感受野级联模块和一个具有非局部注意力的特征融合模块。我们的网络展示了卓越的准确性和推理速度,在 CrackForest、CrackTree、CDD 和 Crack500 数据集上分别实现了 55.60%、59.01%、75.65% 和 57.95% 的 IoU。它每秒还能处理 143 幅图像。实验结果和分析验证了我们方法的有效性。这项工作有助于推动道路裂缝检测的发展,为道路维护和安全改善提供有价值的工具。
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来源期刊
Journal of Real-Time Image Processing
Journal of Real-Time Image Processing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
6.80
自引率
6.70%
发文量
68
审稿时长
6 months
期刊介绍: Due to rapid advancements in integrated circuit technology, the rich theoretical results that have been developed by the image and video processing research community are now being increasingly applied in practical systems to solve real-world image and video processing problems. Such systems involve constraints placed not only on their size, cost, and power consumption, but also on the timeliness of the image data processed. Examples of such systems are mobile phones, digital still/video/cell-phone cameras, portable media players, personal digital assistants, high-definition television, video surveillance systems, industrial visual inspection systems, medical imaging devices, vision-guided autonomous robots, spectral imaging systems, and many other real-time embedded systems. In these real-time systems, strict timing requirements demand that results are available within a certain interval of time as imposed by the application. It is often the case that an image processing algorithm is developed and proven theoretically sound, presumably with a specific application in mind, but its practical applications and the detailed steps, methodology, and trade-off analysis required to achieve its real-time performance are not fully explored, leaving these critical and usually non-trivial issues for those wishing to employ the algorithm in a real-time system. The Journal of Real-Time Image Processing is intended to bridge the gap between the theory and practice of image processing, serving the greater community of researchers, practicing engineers, and industrial professionals who deal with designing, implementing or utilizing image processing systems which must satisfy real-time design constraints.
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