To better understand the interior walls of the water conveyance tunnel, panoramic images of underwater structures' surface defects are obtained at the cost of resolution. However, the lower resolution often falls short of meeting monitoring requirements. To address the conflict between resolution and image acquisition, a bio-inspired S-FREAK underwater image stitching algorithm is proposed. By simulating the vision system of the underwater creature "horseshoe crab," the algorithm enhances image with adaptive lateral inhibition, highlighting its architectural features, considering the characteristics of low signal-to-noise ratio and low contrast of underwater images . Additionally, the algorithm introduces the fast retina keypoint (FREAK) module, emulating human retina characteristics through scale-invariant feature transform (SIFT), to improve the resolution of key feature points. Finally, random sample consensus (RANSAC) feature filtering and fade in and out fusion methods correct the stitching images. Experimental results show that the enhanced adaptive lateral inhibition mechanism increases the matching logarithm of effective feature points, significantly improves stitching accuracy, and optimizes the final outcome.