Abstract
The article describes the use of deep neural networks to detect small floating objects located in a vessel's path. The research aimed to evaluate the performance of deep neural networks by classifying sea surface images and assigning the level of threat resulting from the detection of objects floating on the water, such as fishing nets, plastic debris, or buoys. Such a solution could function as a decision support system capable of detecting and informing the watch officer or helmsman about possible threats and reducing the risk of overlooking them at a critical moment. Several neural network structures were compared to find the most efficient solution, taking into account the speed and efficiency of network training and its performance during testing. Additional time measurements have been made to test the real-time capabilities of the system. The research results confirm that it is possible to create a practical lightweight detection system with convolutional neural networks that calculates safety level in real time.