The purpose of this paper is to develop an effective and reliable corner detection algorithm so as to extract all the desired corners successfully. In addition, the…
The purpose of this paper is to develop an effective and reliable corner detection algorithm so as to extract all the desired corners successfully. In addition, the influences of edge detection method as well as smoothing technique on the overall performance of corner detection techniques are investigated.
In this paper, an effective corner detection algorithm based on subpixel edge detector and Gaussian filter is presented. First, a subpixel accuracy edge detector is used rather than a pixel accuracy edge detector to detect edges. Second, B‐splines approximation technique is used to eliminate the staircase effect of a digital curve. Third, curvature curve derived from the edges is smoothed by a Gaussian filter. Finally, statistical process control technique is applied to detect vertices.
The results show that spatial‐moment outperforms chain code as an edge detector. Furthermore, the Gaussian filter should be used to smooth curvature curve instead of smoothing the profile of an object, because the former provides greater impact on the corner detection results.
In addition to object recognition, motion tracking and obstacle avoidance, the proposed method also has many important engineering and manufacturing applications such as dimensional measuring, reverse engineering, and machine vision‐based computer numerical control (CNC) machining of polygonal sheet metal parts.
The detection of invisible micro cracks (μ‐cracks) in multi‐crystalline silicon (mc‐si) solar wafers is difficult because of the wafers' heterogeneously textured…
The detection of invisible micro cracks (μ‐cracks) in multi‐crystalline silicon (mc‐si) solar wafers is difficult because of the wafers' heterogeneously textured backgrounds. The difficulty is twofold. First, invisible μ‐cracks must be visualized to imaging devices. Second, an image processing sequence capable of extracting μ‐cracks from the captured images must be developed. The purpose of this paper is to reveal invisible μ‐cracks that lie beneath the surface of mc‐si solar wafers.
To solve the problems, the authors first set up a near infrared (NIR) imaging system to capture images of interior μ‐cracks. After being able to see the invisible μ‐cracks, a region‐growing flaw detection algorithm was then developed to extract μ‐cracks from the captured images.
The experimental results showed that the proposed μ‐cracks inspection system is effective in detecting μ‐cracks. In addition, the system can also be used for the inspection of silicon solar wafers for stain, pinhole, inclusion and macro cracks. The overall accuracy of the defect detection system is 99.85 percent.
At present, the developed prototype system can detect μ‐crack down to 13.4 μm. The inspection resolution is high but the speed is low. However, the limitation on inspection speed can easily be lifted by choosing a higher resolution NIR camera.
Generally, this paper is a great reference for researchers who are interested in developing automatic optical inspection systems for inspecting solar wafer for invisible μ‐cracks.
The research described in this paper makes a step toward developing an effective while low‐cost approach for revealing invisible μ‐crack of mc‐si solar wafers. The advantages provided by the proposed system include excellent crack detection sensitivity, capability of detecting hidden subsurface μ‐cracks, and low cost.