Abstract
Yearly, an amount of funds is spent to achieve defect detection in the principle of infrastructure represented by roads, bridges, and buildings. Urban infrastructure is affected by weather conditions, the natural disasters such as floods and earthquakes. As well as, mistakes during street paving operations and quality of paving materials. Various types of damage may appear in the form of small or vast cracks, which gradually spread, destroying the structure. Therefore, it requires building automatic systems for these inspection operations to guarantee its effectiveness and dependability. Hybrid image processing and machine learning approaches are being applied to guarantee better enhancement outcomes and strength in crack detection. This paper aims to offer a review of road image crack detection techniques that apply image processing with/without machine learning. A total of 32 research articles have been composed and studied for the review which has been issued in publications and conferences in the past years. This research manners a thorough analysis and comparison of various methods to identify the most promising automated methods for crack detection. After analyzing and reviewing previous research using digital image processing methods, it is clear from the results obtained that the best of them is the Franji filter method, whose accuracy is close to 98.7%. While discussing and presenting machine learning techniques and convolutional networks, the deduced results that the best of them is the Support Vector Machine (SVM) technique, whose precision is approximately 98.29%
