A fully automatic algorithm was developed for single-frame detection of minelike objects in realistic shallow water, beach, and nearby land environments. Detection was accomplished in gray scale images, containing representative targets and backgrounds, which had been collected by a down-looking coherent active sensor. The problem was made challenging by low contrast, partly covered targets, and highly cluttered images including beach vegetation and rocks, complicated natural backgrounds, obscuration (replacement noise) by glint from the surface of the water and distortion within it, and 15 kinds of manmade objects. To deal with these challenges, innovations have been made in automatic background cancellation, in the final thresholding to binary, and in shape and veracity clues for the feature vector used in the final classification step. Performance is reported for a representative set of 1024 frames. For the majority of background types, including low conventional signal-to-noise ratio and pervasive instances of clutter and replacement noise patches, the algorithm performed correctly in 92% of the frames. This applies individually to frames identified as 'target' where one or more targets existed, and frames identified as 'notarget' where no targets existed. Target-field detection over multiple frames depends upon reliable single-frame target detection. Despite challenging images, performance of our single-frame algorithm appears sufficient for multiframe target-field detection to proceed with acceptable error rates for the majority of background types encountered in the tests conducted.