对于道路、桥梁和人行道等民用建筑的健康和安全监测任务，确定感兴趣区是图像
高层次语义分析的基本要求。混凝土和沥青结构的主要问题之一是裂缝，它首先损
害了建筑的视觉效果，并进一步可能导致建筑毁坏。因此，提前识别裂缝对于保持
民用和交通基础设施的使用寿命至关重要。传统的视觉检查通常是通过人类的视觉
系统进行的，存在昂贵、耗时且容易出错的问题，因为它取决于人的主观判断。最
近，基于计算机视觉检测算法的重新兴起引起了较大的关注，并逐渐取代了一般在
现场进行的传统视觉检测，从而大大改善了传统检测方法的弱点并减少了其带来的
挑战。基于此，本论文提出了高效、稳健和准确的计算机视觉算法，用于有效的道
路裂缝检测。这些算法建立在循环进化神经网络（RCNN）、生成对抗网络（GAN）和
改进型u-net架构的混合结构上。这些算法是本论文的主要内容，具体解释如下。

本论文首先提出了一种强大的深度表征学习算法，用于高效的道路裂缝检测。裂缝
检测的算法，得益于多通道并行神经网络的混合结构。它利用并行处理单元来准确地
进行图像处理和分析。注意力机制的进一步引入，使模型训练专注于小而关键的数
据，从而提高模型的性能。贝叶斯优化算法（BOA）被用来优化多通道并行卷积神经
网络的训练，用尽可能少的神经网络层数来实现更高的准确性，同时提高了处理效
率，减少了处理时间。实验结果表明，所提出的算法在路面裂缝检测任务中可以达到
95%左右的准确率，足以取代现场进行的传统人工检测。

此外，本论文提出了一种增强的深度学习（DL）方法RCNN-GAN，用于检测道路裂
缝。它结合了RCNN和GAN两种有效的技术，并减少了层数，以提高道路裂缝的检测精
度。RCNN是一种物体检测模型，它使用多个卷积层自下而上地提出候选区域，以便对
物体进行定位和分割。网络通过选择性搜索来检测候选区域，通过定义兴趣区(ROI）
的边界来检测任意输入图像中的目标物体。然后从每个区域独立提取特征进行分
类。GAN部署了无监督的机器学习方法，自动挖掘和学习输入数据中的规律或模式，
从而使模型可以用来生成新的例子，并且这些例子可以从原始数据集中提取。通过实
验，我们发现RCNN和GAN的组合为模型提供了更好的性能。

此外，本论文提出了一种改进的u-net结构，用于路面裂缝检测。该架构由于
"U"型结构而得名。它由三个部分组成：收缩、瓶颈和扩展部分。收缩部分由许多收
缩块组成。每个区块接受一个输入，应用两个3X3卷积层，最后连接一个2X2最大池化
层。每个区块后的卷积核或特征图的数量翻倍，这样架构可以有效地学习复杂结构。
最底层在收缩层和扩展层之间进行调解。它使用两个3X3 卷积层，然后连接一个2X2
解卷积层。所提出的u-net架构通过对道路图像的检测和分类来检测道路表面的裂
缝，从而确定某一特定图像是否代表裂缝。

本论文的工作表明，在图像数据集复杂的动态模式下，计算机视觉算法可以在很
大程度上提高路面裂缝检测的性能，并有强大的理论保证，以满足现代计算机视觉应
用的要求。具体来说，算法的有效性、可扩展性、鲁棒性和效率，以及图像数据集多
样性都是需要考虑的问题。本论文的工作通过对大规模真实世界的图像数据集进行广
泛的实验和严格的评估，从理论上证明并验证了所提出的计算机视觉算法的有效性。
不同任务和数据集的实验表明，在道路裂缝检测任务中，与最先进的方法相比，所提
出的深度学习框架具有广泛的适用性、强大的泛化能力以及准确和卓越的性能。

For Health and safety monitoring in civil constructions such as roads, bridges and culvets, identifying the region of interest is the fundamental requirement for image analysis at high-level semantic. One of the major structual problems in concrete and asphat structures is cracks which starts with harming the visual aspect of the construction and further lead to failure of the construction. Therefore, early identification of cracks is vital to maintain the service life of civil and transportation infrastuctures. Traditional visual inspection of road cracks which is ussually conducted through human visualization is expensive, time consuming and prone to errors as it depends on human judgements. The recent resurgence of Computer Vision (CV) based inspection has attracted a considerable
attention and is progressively replacing traditional visual inspection which is normally conducted on-site, thereby handling considerable weakness and reducing challenges posed by traditional inspection methods. In view of that, the work in this dissertation proposes
various efficient, robust and accurate CV algorithms for effective road cracks detection built on hybrid structures of Recurrent-Convolutional Neural Networks (RCNN), generative adversarial networks (GAN) and modified u-net architectures. These algorithms represents the main focus of this dissertation as explained in the following;

The dissertation firstly proposes a robust deep representation learning algorithm for efficient road crack detection, benefiting from hybrid structures of multichannel parallel Convolutional Neural Networks (CNN). A unique hybrid framework is introduced, which utilizes low processing units to accurately perform image processing and analysis. Attention mechanism is further introduced allowing the model training to focus on small but important dataset with increased performance of the model. Bayesian Optimization Algorithm (BOA) were used to optimize the multichannel parallel Convolutional neural networks training with the fewest possible neural network layers to achieve maximum accuracy, improved efficiency and minimum processing time. Experimental results shows that, the proposed algorithm can achieve high accuracy around 95% in road surface cracks detection task which is good enough to replace traditional human inspection.

Furthermore, this dissertation proposes RCNN-GAN which is an enhanced deep learning (DL) approach towards the detection of road crack. RCNN-GAN is deep learning based crack detection which combines two effective techniques RCNN and GAN with reduced
layers to improve road cracks detection accuracy. RCNN is an object detection model that uses high-capacity CNNs to bottom-up region proposals to localize and segment objects. It deploy a selective search to detect the regional proposal network to detect object in any input
image by defining boundaries to the Region of Interest (ROI) and then extracts features from each region independently for classification. GAN deploys unsupervised machine learning approach, that involves automatically discovering and learning the regularities or patterns in
input data in such a way that the model can be used to generate new examples that plausibly could have been drawn from the original dataset. Through experiments, It has been found out that the combination of RCNN and GAN provides improved performance of the model.

Additionally, this dissertation proposes a modified u-net architecture for road surface cracks detection, The architecture looks like a‘U’which justifies its name. This architecture consists of three sections: The contraction, The bottleneck, and the expansion section. The contraction section is made of many contraction blocks. Each block takes an input applies two 3×3 convolution layers followed by a 2×2 max pooling. The number of kernels or feature maps after each block doubles so that architecture can learn the complex structures effectively. The bottom most layer mediates between the contraction layer and the expansion
layer. It uses two 3×3 CNN layers followed by 2×2 up convolution layer. The proposed u-net architecture detects cracks on the road surfaces by detection and classification of the road images thereby determining whether a particular image represents cracks or not.

The work in this dissertation shows that computer vision algorithms can improve the performance of road surface cracks detection considerably with strong theoretical guarantees under complex dynamic patterns and variabilities in image datasets to meet the requirements of modern computer vision applications. Specifically, fundamental characteristic features such as effectiveness, scalability, robustness and efficiency against modern dataset and handling various image dataset. The dissertation demonstrates and validates empirically the effectiveness of the proposed computer vision algorithms via extensive experimental and
rigorous evaluation on massive large-scale real-world image dataset. Experiments across different tasks and dataset show applicability, robust generalization, accurancy and superior performance of proposed DL frameworks compared to the well-known state-of-the-art methods in road crack detection tasks.

 

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