A simple and computationally efficient method was presented for detecting visually salient objects in infrared radiation images. The proposed method can be divided into three steps. Firstly, the infrared image was pre-processed to increase the contrast between objects and background. Secondly, the spectral residual of the pre-processed image was extracted in the log spectrum, then via corresponding inverse transform and threshold segmentation we could get the rough regions of the salient objects. Finally, a sliding window was applied to acquire the explicit position of the salient objects using the probabilistic interpretation of the semi-local feature contrast which was estimated by comparing the gray level distribution of the object and the surrounding area in the original image. And changing the size of the sliding window, different size of objects could be found out. The method was tested on abundant amount of infrared radiation images, and the results show that the saliency detection based object detection method is effective and robust.