A more effective and accurate improved Sobel algorithm has been developed to detect surface defects on heavy rails. The proposed method can make up for the mere sensitivity to X and Y directions of the Sobel algorithm...A more effective and accurate improved Sobel algorithm has been developed to detect surface defects on heavy rails. The proposed method can make up for the mere sensitivity to X and Y directions of the Sobel algorithm by adding six templates at different directions. Meanwhile, an experimental platform for detecting surface defects consisting of the bed-jig, image-forming system with CCD cameras and light sources, parallel computer system and cable system has been constructed. The detection results of the backfin defects show that the improved Sobel algorithm can achieve an accurate and efficient positioning with decreasing interference noises to the defect edge. It can also extract more precise features and characteristic parameters of the backfin defect. Furthermore, the BP neural network adopted for defects classification with the inputting characteristic parameters of improved Sobel algorithm can obtain the optimal training precision of 0.0095827 with 106 iterative steps and time of 3 s less than Sobel algorithm with 146 steps and 5 s. Finally, an enhanced identification rate of 10% for the defects is also confirmed after the Sobel algorithm is improved.展开更多
The oceanic front is a narrow zone in which water properties change abruptly within a short distance.The sea surface temperature(SST) front is an important type of oceanic front,which plays a significant role in many ...The oceanic front is a narrow zone in which water properties change abruptly within a short distance.The sea surface temperature(SST) front is an important type of oceanic front,which plays a significant role in many fields including fisheries,the military,and industry.Satellite-derived SST images have been used widely for front detection,although these data are susceptible to influence by many objective factors such as clouds,which can cause missing data and a reduction in front detection accuracy.However,front detection in a single SST image cannot fully reflect its temporal variability and therefore,the long-term mean frequency of occurrence of SST fronts and their gradients are often used to analyze the variations of fronts over time.In this paper,an SST front composite algorithm is proposed that exploits the frontal average gradient and frequency more effectively.Through experiments based on MODIS Terra and Aqua data,we verified that fronts could be distinguished better by using the proposed algorithm.Additionally through its use,we analyzed the monthly variations of fronts in the Bohai,Yellow,and East China Seas,based on Terra data from 2000 to 2013.展开更多
基金Project(51174151)supported by the National Natural Science Foundation of ChinaProject(2010Z19003)supported by the Major Scientific Research Program of Hubei Provincial Department of Education,ChinaProject(2010CDB03403)supported by the Natural Science Foundation of Science and Technology Department of Hubei Province,China
摘要A more effective and accurate improved Sobel algorithm has been developed to detect surface defects on heavy rails. The proposed method can make up for the mere sensitivity to X and Y directions of the Sobel algorithm by adding six templates at different directions. Meanwhile, an experimental platform for detecting surface defects consisting of the bed-jig, image-forming system with CCD cameras and light sources, parallel computer system and cable system has been constructed. The detection results of the backfin defects show that the improved Sobel algorithm can achieve an accurate and efficient positioning with decreasing interference noises to the defect edge. It can also extract more precise features and characteristic parameters of the backfin defect. Furthermore, the BP neural network adopted for defects classification with the inputting characteristic parameters of improved Sobel algorithm can obtain the optimal training precision of 0.0095827 with 106 iterative steps and time of 3 s less than Sobel algorithm with 146 steps and 5 s. Finally, an enhanced identification rate of 10% for the defects is also confirmed after the Sobel algorithm is improved.
基金Supported by the National Natural Science Foundation of China(No.41271409)
摘要The oceanic front is a narrow zone in which water properties change abruptly within a short distance.The sea surface temperature(SST) front is an important type of oceanic front,which plays a significant role in many fields including fisheries,the military,and industry.Satellite-derived SST images have been used widely for front detection,although these data are susceptible to influence by many objective factors such as clouds,which can cause missing data and a reduction in front detection accuracy.However,front detection in a single SST image cannot fully reflect its temporal variability and therefore,the long-term mean frequency of occurrence of SST fronts and their gradients are often used to analyze the variations of fronts over time.In this paper,an SST front composite algorithm is proposed that exploits the frontal average gradient and frequency more effectively.Through experiments based on MODIS Terra and Aqua data,we verified that fronts could be distinguished better by using the proposed algorithm.Additionally through its use,we analyzed the monthly variations of fronts in the Bohai,Yellow,and East China Seas,based on Terra data from 2000 to 2013.