Abstract: When the random error is not subject to normal distribution,the least absolute deviation estimation is superior to the least squares estimation.In addition,the robustness of the least absolute deviation estimation is also better than that of the least squares estimation.Thus,a method of weak and small target detection in infrared image sequences is proposed based on the least absolute deviation background prediction and the genetic algorithm.Firstly,a prediction model of the background signal based on the least absolute deviation criterion is founded.The extreme value is extracted by the genetic algorithm to predict the background.Then,the estimated image subtracted from the source image gives the residual image.The residual image is segmented using the threshold selection algorithm based on the two-dimensional exponent entropy.The experimental results with some real infrared image sequences show that the proposed method reduces the false alarm rate and greatly improves the detection performance of weak and small targets compared with the method based on the least squares background predication.