The mine countermeasure operations consist of hunting i.e. searching for possible mines from the seabed and water column as well as demolition or disposal of found mines to enable safe passage of other naval units. Mine hunting is a task conducted by specialized naval units aiming to clear sea areas from estimated or realized naval mine threats. The search is usually initiated by sweeping broad areas with a side scan sonar to produce an initial picture of the seabed and localize possible threats for closer examination. The manual analysis process is challenging and time-consuming, especially in environments where seabeds are littered with mine-like objects such as rocks and other roughness. This paper researches the possibility of enhancing littoral mine hunting with object detection using a deep convolutional neural network and vision transformer methods separately and as a Mixture of Experts system to do the preliminary object detection and classification of the data multiples faster than a sole sonar operator. The results indicate that even a processed sonar image can be used to enhance mine hunting operations in a transparent, explainable manner but would benefit from enriching with additional data and signal analysis to ensure the detection of the most difficult targets.