Joint scanning thermography(JST) can detect defection of large-area materials. The defection of raw images is inaccurate and the quantitative analysis is hard to achieve. According to the characteristics of images from the reconstruction of joint scanning thermography, a method based on an one-dimensional convolutional neural network ( 1D-CNN) is proposed to detect and quantitate defects. The one-dimensional temperature time series corresponding to the pixels of the pulse image sequences is applied as inputs for the network. This method could achieve defect detection automatically and defect quantification for carbon fiber reinforced polymer. As the result indicated, the 1D-CNN based method could detect defection automatically and accurately. It has a 98. 8% accuracy in defect classifying of training set and an about 70% accuracy in defect classifying of training set. The result is better than traditional method.