In recent years, unmanned aerial vehicles (UAVs) have been increased in civil engineering and infrastructure maintenance due to their potential to detect cracks in asphalt pavement, especially in riparian areas. Vegetation growth in riparian areas can create benefits for the infrastructures, e.g. embankment consolidation, but can also cause cracks and potholes to form. Local authorities should proactively manage vegetation growth in the riparian asphalt pavement to avoid these adverse effects. The monitoring before the maintenance operation also needs lots of time, and the efficient approach to understanding the work amount in the large-scale area is considered. UAVs assisted with computer vision algorithms, such as the You Only Look Once version 7 (YOLOv7) object detection model, have shown great potential in detecting and segmenting riparian road asphalt pavement cracks. This approach cannot just locate the cracks and also segment the cracks in the instances with pixel (px)-based sizes. This study provides three models derived from the divided dataset, one custom dataset with several bounding box sizes (i.e., 20-, 30-, 100-px), and two public datasets using asphalt pavement surface damage type and instances to annotate the crack. Based on the above results, the resulting inference was taken to compare with the True Label in mesh and had around 90% accuracy (i.e., Recall and F1).