From the algorithm description of fault bucket tooth positioning designed, it can be seen that the analysis logic of fault location of broken 2 teeth is obviously complex, which is related to the case of broken 1 tooth. Some fault conditions of broken 1 tooth can be directly judged through the spacing relationship between excavator bucket teeth, while the fault conditions of broken 2 excavator bucket teeth need to determine whether there are excavator bucket teeth at the end, that is, the difference of contour characteristics between bucket teeth should be calculated first, The extraction of bucket tooth contour depends on the performance of image segmentation. When the bucket tooth of excavator is distorted greatly due to shooting, or the segmentation result of bucket tooth of excavator is not fine enough, it is easy to make mistakes.
Through the result analysis. The test images of fault detection errors mainly focus on the broken teeth at both ends. There are two main reasons. The first is that the acquisition angle of the selected detected image is quite different from the template image, resulting in large affine distortion of the detected excavator bucket teeth relative to the template. Therefore, it is impossible to distinguish the bucket teeth at the end of the bucket from the middle bucket teeth.
The second is that the segmentation result of the image segmentation program is not fine enough, which leads to the distance between the extracted contour feature and the excavator bucket tooth feature of the template image is too large, resulting in the error of fault detection.
To sum up, according to the test experiment, the target detection program has a good detection effect on the experimental bucket and can accurately identify the position of the bucket teeth of the excavator. The performance of image segmentation and the pose of the bucket have a great impact on the detection effect of the fault detection program.
When the pose difference between the template bucket and the fault bucket is large, it will lead to affine distortion of the bucket teeth of the excavator. In this paper, the standard Fourier descriptor is used as the contour feature (with rotation, scaling and displacement invariance), which has limited invariance and does not have affine invariance. Considering that the camera is fixed in the actual shooting scene, and the detection occurs at a fixed time, the distortion of excavator bucket teeth is small, so the method in this paper has a certain reference.