Three-dimensional surface defect inspection remains a challenging task. This paper describes a novel automatic vision-based inspection system that is capable of detecting and characterizing defects on an airplane exterior surface. By analyzing 3D data collected with a 3D scanner, our method aims to identify and extract the information about the undesired defects such as dents, protrusions or scratches based on local surface properties. Surface dents and protrusions are identified as the deviations from an ideal, smooth surface. Given an unorganized point cloud, we first smooth noisy data by using Moving Least Squares algorithm. The curvature and normal information are then estimated at every point in the input data. As a next step, Region Growing segmentation algorithm divides the point cloud into defective and non-defective regions using the local normal and curvature information. Further, the convex hull around each defective region is calculated in order to englobe the suspicious irregularity. Finally, we use our new technique to measure the dimension, depth, and orientation of the defects. We tested and validated our novel approach on real aircraft data obtained from an Airbus A320, for di↵erent types of defect. The accuracy of the system is evaluated by comparing the measurements of our approach with ground truth measurements obtained by a high-accuracy measuring device. The result shows that our work is robust, e↵ective and promising for industrial applications.

Structured light based patterns provide a means to capture the state of an object shape. However it may be inefficient when the object is freely moving, when its surface contains high curvature parts or in out of depth of field situations. For image-based robotic guidance in unstructured and dynamic environment, only one shot is required for capturing the shape of a moving region-of-interest. Then robust patterns and real-time capabilities must be targeted. To this end, we have developed a novel technique for the generation of coded patterns directly driven by the Hamming distance. The counterpart is the big amount of codes the coding/decoding algorithms have to face with a high desired Hamming distance. We show that the mean Hamming distance is a useful criterion for driving the patterns generation process and we give a way to predict its value. Furthermore, to ensure local uniqueness of codewords with consideration of many incomplete ones, the Perfect Map theory is involved. Then, we describe a pseudorandom/exhaustive algorithm to build patterns with more than 200×200 features, in a very short time, thanks to a splitting strategy which performs the Hamming tests in the codeword space instead of the pattern array. This leads to a significant reduction of the computational complexity and it may be applied to other purposes. Finally, real-time reconstructions from single images are reported and results are compared to the best known which are outperformed in many cases.

In this paper we present a new 3-D laparoscopic device based on structured light for minimally invasive surgery. Real-time reconstruction of internal organs' surfaces is very challenging as the numerous geometric and photometric variabilities and disturbances (bloody parts, specularities, smokes,...) often occur during the surgical operation, sometimes with manipulations by several assistants. We then conceived a structured light vision system to illuminate a coded pattern by means of an external video projector device or miniaturized diffractive optical elements and a laser source. Among the structured light techniques, the spatial neighbourhood scheme is the most relevant class of approaches to deal with moving and deformable surfaces, then to capture the depth map with only one shot. Each neighbourhood (a (3 × 3) window) is representing a codeword of length 9, and is unique in the whole pattern, even if there is a lack of information. To do so, a monochromatic subperfect map-based pattern is computed, driven by a desired minimal Hamming distance, H(min), between any couple of codewords. This provides patterns with high correction capabilities (H(min) > 1). For practical considerations, each numerical codeword symbol is associated to a unique visual feature embedding the local orientation of the pattern, which is helpful for the neighbourhood retrieval during the decoding process. Together with the endoscopic device, in vivo real-time reconstructions (in mini-invasive surgical conditions) are presented to assess both the efficiency of the proposed pattern design, the decoding process and the 3-D laparoscope setup realized in the lab.