Joint Inspection of HD Video and Robot in Substation Based on OCR Technology

The research on the joint inspection technology of HD video and robot in substation is of great significance in the operation and maintenance management of substation. The related research on multidimensional collaborative inspection technology of substation has attracted great attention. The substation monitoring system is designed to realize multidimensional collaborative inspection of HD video and inspection robot. OCR technology is used to improve the abnormal recognition system of inspection robot equipment. According to the collection of video information, DSP comprehensive information processing is carried out, and the detection information is analyzed by frequency domain filtering method through human-computer interaction interface. Meanwhile, the main component features of substation detection components are extracted by pattern recognition method, and the resolution model of similar features of video monitoring images is constructed. It plays an important role in the daily operation and maintenance of substation to construct the reliability visual tracking and identification model and optimize the multidimensional cooperative inspection system model.

A novel method of text detection in radiographic images

With the rapid development of industries such as nuclear power and shipbuilding, radiographic testing (RT) is widely used in these fields as an important means of weld inspection. It also produces a large number of radiographic films, which consume a great deal of manpower and material resources. It is therefore beneficial for the radiographic film to be digitised for storage and archiving. Text detection in RT weld images is an important prerequisite for the archiving of digitised films. This paper proposes a novel text detection method that employs mask convolution and frequency-domain filtering, which can detect text at different positions, with different fonts and of different sizes in RT weld images. The method is evaluated using 366 different images and shows significant efficacy for text detection in RT weld images, with the precision value reaching 96%. The method used in this paper is also compared with other methods that are commonly used in other fields and the results show that the proposed method gives improved results compared to state-of-the-art methods.