The duty of detecting pavement cracks is crucial for ensuring traffic safety. The process of manually detecting cracks takes a long time. To accelerate this process, an autonomous road fracture identification technology is necessary. However, because of the intensity inhomogeneity of cracks and the intricacy of the background, such as poor contrast with neighbouring pavements and probable shadows of similar intensity, it remains a difficult work. Recent breakthroughs in deep learning in computer vision have inspired us, so we will be using the most accurate method that involves deep learning to identify the gap in the pavement. In this research paper, we have compared various object detection techniques such as aYOLO v5 and Single Shot Detector(SSD) and come to a conclusion that SSD is most suitable for our purpose. It will also help for road safety purposes. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.