The invention discloses a
point cloud differential defect detection
system and detection method based on multi-
feature fusion, which are mainly used for high-precision detection of surface defects of a side wallboard of a
railway passenger car, and the method comprises the following steps: obtaining datum
point cloud data through a designed standard side wallboard model of the
railway passenger car, and collecting real-time
point cloud data through a
laser scanner; preprocessing the obtained point
cloud data; calculating the difference between the real-time point cloud and the reference point cloud by adopting a difference method for fusing geometric features such as
Euclidean distance, curvature and normal vector, namely calculating the
Euclidean distance difference, curvature difference and normal vector difference between the reference point cloud and the real-time point cloud; and a comprehensive abnormal
score is generated by further combining a dynamic weight
fusion mechanism, and different region characteristics (such as a plane, a curved surface and an
edge region) are adapted through dynamic threshold adjustment. According to the method, the
defect region is segmented through the clustering
algorithm, and the defect area and depth are quantified. The method has the advantages of high detection precision, strong adaptability and good real-time performance, and is suitable for complex surface defect detection in industrial production.