The renewable energy sources appear as alternatives that do not pollute the atmospheric air, avoiding the emission of gases that cause the greenhouse effect, among other benefits such as the inexhaustible availability of these sources. Infrared thermography has gained important prominence among the non-destructive tests used in the quality control of wind turbine blades (Wind Turbine Blades – WTB). Some decades, a global trend for has been the use of the concept of total quality, which attributes benefits such as zero-defect manufacturing, productivity gains, elimination of unnecessary costs, in addition to allowing greater safety, which is why it is a very popular concept. by the aerospace industry. One of the biggest challenges in wind blade inspections to reach total quality is the implementation of inspection tasks along the production line through computer vision. This work proposes the use of an active infrared thermography method with the aid of a computer vision system to identify defects in samples of the Glass Fiber Reinforced Polymer (GFRP) composite used in the manufacture of wind turbine blades. Algorithms implemented in Python programming language are used with the help of routines from the OpenCV library to detect internal defects varying diameters and depths. The results of an experimental test are evaluated to validate the implemented computer vision system, observing the performance of the techniques used.
