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From the comparisons mentioned above, it can be seen that the proposed crack detection method has achieved satisfactory detection results because of the following two aspects: (1) the combined complementary image region features (i.e., LBP and HOG) have a strong discriminative capability for dealing with the various background noises; (2) the developed multi-task learning framework contributes to the robustness of the crack region detector when addressing the complex background disturbances. What is noteworthy is that the advocated crack detection method cannot always acquire the perfect crack region detection results. For instance, as illustrated in Figure 10 (3), the lower-right crack regions (see the dashed ellipse) of the input image are not detected. From the point of view of appearance modeling, the visual aspects of undetected tiny crack regions are very similar to those of some crack-like feature (e.g., stripes in Figure 10 (4)). Therefore, to adapt better to complicated surroundings, it is likely that these ambiguous potential crack regions are mistakes.
Table 2 shows the training time of each crack detection in dealing with the same amount of image region data. Moreover, the code implementation software also has an effect on the training efficiency. Although all the compared methods were implemented in MATLAB, there are still some differences in carrying out the specific crack detection, which are listed at the bottom of Table 2. 2ff7e9595c
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