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Natural-like technosignatures candidates may represent a detection problem for both artificial systems and humans. We tested traditional computer vision models with natural formations with special characteristics, Ahuna Mons region in Ceres in this particular case. We looked if these artificial models may represent a trustful aid to human detection and identification of potential technosignatures in planetary surfaces. Ahuna Mons is a 4km particular geologic feature on the surface of Ceres of possibly cryovolcanic origin. The special characteristics of Ahuna Mons are also interesting in regard of its surrounding area, especially for the big crater besides. This crater possesses similarities with Ahuna Mons including diameter, age, morphology, etc. Under the cognitive psychology perspective and using current computer vision models we analysed these two features on Ceres for comparison and pattern recognition similarities. Several algorithms were employed avoiding human cognitive bias. 3D analysis from images of both features characteristics are discussed. Results showed positive results for these algorithms about similarities of both features. Discussion is provided about implications of this pilot computer vision techniques experiment for Ahuna Mons and the potential cognitive bias problem of both human and Artificial Intelligence models and the risks for the search of technosignatures.