Finding Concrete Cracks in a Building using Improved Deep Learning Algorithms

To ensure functionality and durability, engineering constructions must have fractures identified and repaired. Since cracks are typically discovered through visual inspection, the examiner's personal judgment plays a vital part in both the detection of concrete cracks and the estimation of their characteristics. In the past, this has helped with both the evaluation of the crack phenomenon and the repair of various structures. The number of engineering structures has expanded as a result of industrial development, but relative to that, the level of skill in the crack-detecting field has not improved. Therefore, a quicker yet more precise method is required. The image processing method detects the cracks and their characteristics simultaneously. The formulation of the algorithm and the implementation is simple. However, other flaws, such as mistaking noises for fractures and a lack of accuracy in spotting micro-cracks, have turned into major obstacles for this technology. Finding transverse cracks in concrete structures is another critical problem. Therefore, a thorough analysis of the most recent articles is required to establish an accurate technique. In order to comprehend the potential of this technology, a critical study of the image-processing phenomenon and a thorough literature review has been done in this paper. A universal DenseNet121 classification-based algorithm with camera pictures for crack identification could be an effective strategy with greater accuracy.