Abstract: In the panoramic field-of-view (FOV) infrared imaging search system (PIRSS), the infrared image background was especially complicated, along with rapidly increasing of the data quantity. According to the infrared image's characteristics in the PIRSS, a flow of detecting algorithm based on the weighted local entropy (WLE) matrix of image blocks was proposed. Firstly, it established the image blocks matrix for the entire image, which was based on the spatial distributing characteristics of the panoramic image. Then a new characteristic function called weighted local entropy was presented, and calculated the WLE matrix for the image blocks. Finally, an appropriate adaptive threshold method based on the analysis of WLE matrix was adopted, which implemented the region separation of candidate targets from background and obtained the ROI. Experimental results demonstrated that the proposed algorithm was effective and befitting for the infrared target detection in large FOV. It also has good performance for real-time processing and engineering realization.