In this paper,active infrared thermography is used to detect material defects.Under the premise of controlling the influence of the external environment,the effects of two detection factors,thermal excitation time and the detection distance,are experimentally studied and analyzed.The infrared image sequence of the glass fiber reinforced plastic test block with flat bottom holes under the best parameters is acquired synchronously.The infrared image sequence usually contains hundreds or thousands of frames of images.In order to obtain the main information representing the entire image sequence,the principal component analysis algorithm (PCA) is used to process the image sequence.The influence of the fusion interval on the processing results of the PCA algorithm is explored for the first time,and the fusion interval can be selected according to the percentage of the temperature peak drop.The objective and subjective comparative evaluation and analysis of the processing results are also made.The experimental results show that when the peak temperature difference drops to 80%,selecting the sequence image frame number greater than this value as the fusion interval,the PCA processing has the best effect.Finally,the strategy is discussed to reduce the influence factors,such as the reflection of the infrared lens,in PCA processing.