Abstract
Tool safety is an important part of machining and machine tool safety, and machine tool path image detection can effectively obtain the in-machine condition of a tool. To obtain an accurate image edge and improve image processing accuracy, a novel subpixel edge detection method is proposed in this study. The precontour is segmented by binarization, the second derivative in the neighborhood of the demand point is calculated, and the obtained value is sampled according to the specified rules for curve fitting. The point whose curve ordinate is 0 is the subpixel position. The experiment proves that an improved subpixel edge can be obtained. Results show that the proposed method can extract a satisfactory subpixel contour, which is more accurate and reliable than the edge results obtained by several current pixel-level operators, such as the Canny operator, and can be used in edge detection with high-accuracy requirements, such as the contour detection of online tools.
Highlights
With the continuous improvement of machining accuracy, especially precision machining, increasing tool chip parameters and optical imaging system precision is limited.us, under the same conditions, subpixel subdivision can improve measurement accuracy and reasonably control production cost. erefore, examining a set of mature and stable subpixel contour detection algorithms is of considerable significance. is study conducts in-depth research on existing subpixel detection algorithms and proposes algorithm optimization for the environmental application of numerical control tools.Image edge detection in computer image processing is a technology that was developed in the past but is continuously advancing
Us, under the same conditions, subpixel subdivision can improve measurement accuracy and reasonably control production cost. erefore, examining a set of mature and stable subpixel contour detection algorithms is of considerable significance. is study conducts in-depth research on existing subpixel detection algorithms and proposes algorithm optimization for the environmental application of numerical control tools
Feng et al [3] proposed a saliency detection method based on the histogram contrast algorithm and wireless multimedia sensor network (WMSN) image to process animal images
Summary
With the continuous improvement of machining accuracy, especially precision machining, increasing tool chip parameters and optical imaging system precision is limited. Is study conducts in-depth research on existing subpixel detection algorithms and proposes algorithm optimization for the environmental application of numerical control tools. Image edge detection in computer image processing is a technology that was developed in the past but is continuously advancing. With the improvement of the accuracy requirements of target edge detection, the pixel-level edge obtained by common operators cannot meet the needs of precision measurement and visual calibration. Wu proposed a subpixel edge detection algorithm based on Franklin’s matrix, which was fast and accurate and Computational Intelligence and Neuroscience demonstrated strong noise immunity [17]. A tool wear measurement system based on machine vision was developed to solve the problems of manual operation and shutdown detection in actual production [20]. Wu proposed a laser image subpixel edge detection method based on Gabor filtering and mathematical morphology [21]. Ai et al proposed a corner detection algorithm based on subpixel edges [22]
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