Detection is one of the most formidable problems in automatic target recognition, since it involves locating multiple classes of targets of interest with distortions present in cluttered scenes. Fast and efficient algorithms are needed for detection, since in detection we need to analyze every local region of large image scenes. Minimum noise and correlation energy (MINACE) filters are attractive distortion-invariant filters (DIFs); we consider MINACE filter use in detection, since they provide sharp correlation peak values for targets and overcome the effect of aspect view distortions in the input data. Most prior work on DIFs considered classification, not detection. MINACE filters seem to require fewer filters than do other DIFs, and they recognize objects with aspect views different by 15° from those present in the training set. They are also shift-invariant and require only a few filters to handle detection of multiple target classes. We test our improved MINACE filters to detect 8 classes of objects in an infrared (IR) database with a ±90° range of aspect views. Initial test results are excellent with only 3 filters needed and very low false alarm rates.