Technologies related to decision support systems in intelligent vessels have reached a high level of maturity in recent years. Meanwhile, autonomous and unmanned vessels have been extensively studied in conjunction with autonomous vehicles using path planning, trajectory generation, location, and logistic optimization. Numerous technologies have been developed for the planning of path and obstacle avoidance strategies in the sea and rivers, but the challenge remains when it comes to avoiding marine mammals, especially under the constraint of optimal trajectory to reduce energy consumption and time. This paper proposes a planner based on both neural network-driven and deterministic approaches. An Attention U-Net is used for semantic segmentation of terrestrial and aquatic areas, followed by the implementation of an artificial potential field to represent the map. Skeletonization and weighted dilation are then carried out to generate an optimal cost map for \(Theta^*\) path planning. The proposed Navigation Aid System includes parameters related to the minimum distance from the shore, a maximization of cetacean distance under the constraint of minimizing the total distance traveled by the vessel. The pilot has the option to add waypoints and force the algorithm to reach these locations using a minimal traveled distance. The results show an optimal vessel path for the segmented map, given the constraints.