Railway wagon derailment automatic brake valve cock handle closing fault image recognition method
An automatic braking valve, railway freight car technology, applied in image enhancement, image analysis, image data processing and other directions, can solve the problem of low image recognition accuracy, and achieve the effect of improving operation quality, improving operation efficiency and high accuracy
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specific Embodiment approach 1
[0026] Specific implementation mode 1: In this implementation mode, the specific process of the fault image recognition method for the derailment automatic brake valve plug door handle of the railway freight car is as follows:
[0027] Step 1. Set up high-definition equipment around the truck tracks to take pictures of the trucks passing by at high speed, and obtain two-dimensional images on both sides of the trucks; use line scanning to achieve seamless image stitching and generate two-dimensional images with a large field of view and high precision .
[0028] Step 2. Rough positioning of the derailment automatic brake valve plug door handle components on the two-dimensional image obtained in step 1;
[0029] Step 3. Establish the original sample data set based on the rough positioning of the derailed automatic brake valve plug door handle part image in step 2; the specific process is:
[0030] As truck components may be affected by rain, mud stains, oil stains, black paint ...
specific Embodiment approach 2
[0042] Embodiment 2: This embodiment differs from Embodiment 1 in that in Step 2, the two-dimensional image obtained in Step 1 is used for rough positioning of the derailment automatic braking valve plug handle; the specific process is:
[0043] Roughly locate the position of the parts according to the wheelbase information and model information of the truck, and intercept the local area image including the derailed automatic brake valve plug handle part from the side two-dimensional image, which can effectively reduce the time required for fault identification and improve identification Accuracy.
[0044] Other steps and parameters are the same as those in Embodiment 1.
specific Embodiment approach 3
[0045] Specific embodiment three: the difference between this embodiment and specific embodiment one or two is that the data set is enlarged in the step four; the specific process is:
[0046] Although the establishment of the sample data set includes images under various conditions, in order to improve the stability of the algorithm, it is still necessary to perform data amplification on the sample data set. The form of amplification includes image rotation, translation, scaling, mirroring and other operations, and each operation is performed under random conditions, which can ensure the diversity and applicability of samples to the greatest extent.
[0047] Other steps and parameters are the same as those in Embodiment 1 or Embodiment 2.
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