At present, the test of the stopping distance of the escalator mainly relies on the marking method, which is completed
manually, and the error is large. In order to improve the efficiency and accuracy of detection, an idea and method for
escalator stopping distance measurement based on infrared distance test and Hall induction current test technology is
provided. First, it analyzes the braking principle and braking process of the escalator, adopts infrared-based ranging
technology, and increases the test frequency according to the algorithm to achieve the effect of real-time testing. During
the test, the position of the escalator during the entire braking process is dynamically tracked. Combined with the current
sensor to capture the change of the current, through the extraction and processing of the two signals, the accurate
stopping distance can be calculated. The test results show that the repeat error of the stopping distance measured by this
method is 1.7%, which is reduced to 1/5 of the original, and the test time is shortened to 1/2 of the original.
Aiming at the problem of unmanned aerial vehicle inspection images being susceptible to environmental interference during shooting, resulting in blurry image capture and inability to accurately identify defects in key components of transmission lines, this paper uses SRGAN to super-resolution reconstruction of low-resolution inspection images to improve image quality to meet the needs of deep learning algorithms or manual accurate recognition of line defects. First, a high-resolution image data set of key components of the transmission line is produced, and the data set is obscured as a low-resolution image data set. Then the PaddlePaddle framework is used to build the SRGAN super-resolution network model to perform super-resolution reconstruction on the low-resolution data. In model training, the model parameters are optimized according to the training situation, the optimal model is obtained, and the reconstruction experiment on low-resolution images is performed. The experimental results show that the image generated by SRGAN is similar to the high-resolution image in sharpness, and has achieved good results.
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