###### Abstract  
With the rapid growth in urban construction in Malaysia, road breakage has challenged traditional manual inspection methods. In order to quickly and accurately detect the extent of road breakage, it is crucial to apply automated road crack detection techniques. Researchers have long studied image-based road crack detection techniques, especially the deep learning methods that have emerged in recent years, leading to breakthrough developments in the field. However, many issues remain in road crack detection methods using deep learning techniques. The field lacks state-of-the-art systematic reviews that can scientifically and effectively analyze existing works, document research trends, summarize outstanding research results, and identify remaining shortcomings. To conduct a systematic review of the relevant literature, a bibliometric analysis and a critical analysis of the papers published in the field were performed. VOSviewer and CiteSpace text mining tools were used to analyze and visualize the bibliometric analysis of some parameters derived from the articles. The history and current status of research in the field by authors from all over the world are elucidated and future trends are analyzed. road crack detection; deep learning; scientometric analysis +
Footnote †: journal: Medical  
in pavement management is extremely important to address the challenges posed by the rapid expansion and aging of road infrastructure. These technological advances are becoming increasingly important as the demand for transportation infrastructure continues to grow. They not only play a crucial role in maintaining the infrastructure and improving the durability and reliability of roads but also reduce the rate of traffic accidents brought about by the inspection process. In the long run, these technologies make an outstanding contribution to smarter and more sustainable management of infrastructure, in line with the global trend towards automation and data-driven decision-making in urban planning and development [6]. Crack detection is divided into three steps: first crack image acquisition, then preprocessing of the acquired image, and finally classification, detection, and segmentation of the processed image. Researchers have applied various types of images to the field of cracks, such as RGB (red, green, and blue) images [7], infrared ray images [8], ground-penetrating radar images [9], etc. Image preprocessing methods include grayscale conversion [10], binary conversion [11], k-means clustering [12], fractal dimension, thresholding [13], edge detection [7], etc. Image processing techniques can be effective in extracting features and reducing the negative impact on the environment. Different methods classify, detect, and segment the cracks after extracting the features. In order to enhance crack detection, contemporary researchers are increasingly favoring deep learning-based image recognition algorithms such as convolutional neural network (CNNs) [14], You Only Look Once (YOLO) [9], and U-Net [15], etc. This is due to neural networks' ability to autonomously extract essential features from road crack images in order to detect cracks more accurately. With advancements in image processing algorithms, vision-based crack detection systems are becoming increasingly popular. This particular field has been the focus of a certain number of scholarly articles. However, there is a distinct lack of relevant systematic review papers in the field that provide bibliometric analysis and critical analysis of the existing literature. This paper aims to demonstrate research trends and provide a succinct overview of the field of road defect detection. The authors conducted a systematic review by examining notable papers focusing on image-based crack detection algorithms published between 2013 and 2023. This review aims to fill the gaps in the existing literature and provide valuable information to new researchers in this research area. This work focuses on research papers from 2013 to 2023 as a basis for research in this field.