In order to analyze the development, trend and dynamics of inner surface defect detection, 4 708 relevant literature in English and 818 in Chinese were collected through the search of relevant literature in this field in WoS and CNKI databases. The visual analysis software CiteSpace is used to study the knowledge map of literature co-occurrence and clustering, analyze the distribution status and cooperation of internal surface defect detection in countries, institutions and scholars, and sort out the research hotspots and cutting-edge trends. It is found that the research on inner surface defect detection has obvious interdisciplinary attributes, mainly involving analytical chemistry, material science, spectroscopy, instrumentation, mechanical engineering and computer science. In recent years, the annual growth rate of related literature in the WoS database has been more than 10%, and the annual growth rate of CNKI has been more than 20%. China and the United States have become the most active countries in this field, accounting for about 40% of the total number of publications. Chinese scholars’ research in non-destructive testing, image processing and other fields lags behind that of foreign scholars, but they catch up in machine vision and deep learning. According to the research route, it can be divided into detection based on acousto-optic electrothermal magnetism and detection based on the visual imaging. The former includes the acquisition of spectral, ultrasonic and electromagnetic images by different technical means and the realization of defect detection by image processing technology, while the latter is the main defects recognition and classification based on visual image, has become the main research focus in the field. The development of inner surface defect detection can be divided into three stages: defect identification, defect classification and defect analysis. Before 2000 defects were recognized and determined mainly by thermal, acoustic, optic, electrothermal, and magnetic signals or images. Since 2000, the support vector machine (SVM) technology greatly improves the efficiency and accuracy of defect classification. In recent ten years, with the increasing demand for defect analysis and measurement, defect location and measurement based on machine vision has gradually become a development trend, and the object of defect detection has gradually developed to the inner surface of deep holes and small holes.