To address the challenge of detecting road defects in drone-captured image point clouds, this study introduces a road defect detection method based on point cloud slicing, plane fitting, and clustering. Firstly, drone images are captured to facilitate 3D reconstruction and the generation of image point clouds. Subsequently, point cloud data undergoes slope filtering and statistical outlier filtering to eliminate noise and anomalous data points. Next, the point clouds are sliced, and a random sample consensus (RANSAC) plane fitting algorithm is applied to estimate the road’s plane model. Then, the point cloud DBSCAN clustering algorithm is employed to differentiate between edge noise and road damage point clouds. Finally, the point cloud slicing technique is utilized to assess the extent of the damage. In the experiments, the study employs actual drone-collected point cloud data and compares the proposed method with an approach relying on point cloud verticality features. The experimental results demonstrate that the proposed method exhibits a high level of accuracy and robustness, with a volume estimation error of only 1307 cm³. Compared to traditional methods, the proposed method excels in precisely detecting road damage and adapting to intricate road shape variations.