Automatic darkest filament detection (ADFD): a new algorithm for crack extraction on two-dimensional pavement images

Intelligent Detection Method for Internal Cracks in Aircraft Landing Gear Images under Multimedia Processing

“…In order to highlight the advantages of the proposed method, we compare it with the expert evaluation results of the method proposed by Kaddah et al [6] and the method proposed by Yang et al [7]. The evaluation results of different methods by experts are shown in Table 1: Analyzing the data results in Table 1, it can see that the average score of the expert evaluation result of the proposed method is 93.6, while the average score of the expert evaluation result of the method proposed by Kaddah et al [6] is 84.4, and the average score of the expert evaluation result of the method proposed by Yang et al [7] is 77.3. From the data in the table, it can be seen that the experts have a higher evaluation of the proposed method, indicating that the method has a better crack detection effect.…”

“…In order to further verify the effectiveness of the proposed method, Matlab 7.0 software is used for simulation experiments. Figure 7 is a schematic diagram of the comparison of the crack image edge detection between the proposed method and the method proposed by Kaddah et al [6] and the method proposed by Yang et al [7].…”

“…It can be seen from Figure 7 that the method proposed by Kaddah et al [6] is more sensitive to the internal details of the image cracks, but the image brightness is low, which affects the crack detection results. Although the method proposed by Yang et al [7] maintains the brightness of the original image well, the accuracy of the crack edge location is poor, and the edge information is relatively fuzzy, which has a greater impact on the joint position detection of the landing gear crack, and does not highlight the target center in the image.…”

“…In order to verify the effectiveness and comprehensiveness of the intelligent detection method for internal cracks in aircraft landing gear images under multimedia processing, simulation experiments are carried out. In the experiment, the crack extraction algorithm for two-dimensional road images proposed by Kaddah et al [6] and the oil pipe crack detection algorithm based on improved median filtering and morphology proposed by Yang et al [7] are selected as the comparison method, and the comparison is made from subjective and objective evaluation. The detection results are compared, and the comparison process and results are analyzed as follows [22,23].…”

“…The experimental results show that the method can provide more accurate detection results for two-dimensional road images without supervision. However, because the problem of image denoising is not considered, the edge information of the cracked image is blurred [6]. Yang et al proposed an algorithm for oil pipe crack detection based on improved median filtering and morphology [7].…”