The urban sewage pipeline rupture and leakage can seriously affect the daily life of residents. The defect detection of drainage pipelines is an important support for the continuous functioning and later maintenance o...
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The urban sewage pipeline rupture and leakage can seriously affect the daily life of residents. The defect detection of drainage pipelines is an important support for the continuous functioning and later maintenance of pipelines. The internal environment of the wastewater drainage pipeline is poor. The existing defect detection methods have limited ability to extract image features. The accuracy of the detection methods cannot meet the expected requirements. Based on this, a pipeline defect detection robot is designed based on defect image analysis of waste drainage pipelines. For defect image recognition in robots, the Canny algorithm is improved to pre-process defect images. Then, the improved Mask-region-convolutional neural network (Mask-RCNN) model is constructed to extract the processed defect image features. According to the findings, the defect image recognition model constructed in the study converges only after 12 iterations. The accuracy of image recognition can be maintained at around 98.85%. The robot defect detection rate based on this image recognition model is 100%. This indicates that the proposed detection robot based on improved defect image feature recognition can better complete defect detection of drainage pipelines, providing support for drainage pipeline maintenance.
Visual illusion is the fallacious perception of reality or some actually existing object. In this paper, we imitate the mechanism of Ehrenstein illusion, neon color spreading illusion, watercolor illusion, Kanizsa ill...
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Visual illusion is the fallacious perception of reality or some actually existing object. In this paper, we imitate the mechanism of Ehrenstein illusion, neon color spreading illusion, watercolor illusion, Kanizsa illusion, shifted edges illusion, and hybrid image illusion using the Open Source Computer Vision Library (opencv). We also imitate these illusions using Cellular Neural Networks (CNNs). These imitations suggest that some illusions are processed by high-level brain functions. We next apply the morphological gradient operation to anomalous motion illusions. The processed images are classified into two kinds of images, which correspond to the central drift illusion and the peripheral drift illusion, respectively. It demonstrates that the contrast of the colors plays an important role in the anomalous motion illusion. We also imitate the anomalous motion illusions using both opencv and CNN. These imitations suggest that some visual illusions may be processed by the illusory movement of animations.
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