In the engine room of intelligent ships, visual recognition is an essential technical precondition for automatic inspection. At present, the problems of visual recognition in marine engine rooms include missing detection, low accuracy, slow speed, and imperfect datasets. For these problems, this paper proposes a marine engine room equipment recognition model based on the improved You Only Look Once v5 (YOLOv5) algorithm. The channel pruning method based on batch normalization (BN) layer weight value is used to improve the recognition speed. The complete intersection over union (CIoU) loss function and hard-swish activation function are used to enhance detection accuracy. Meanwhile, soft-NMS is used as the non-maximum suppression (NMS) method to reduce the false rate and missed detection rate. Then, the main equipment in the marine engine room (MEMER) dataset is built. Finally, comparative experiments and ablation experiments are carried out on the MEMER dataset to verify the strategy’s efficacy on the model performance boost. Specifically, this model can accurately detect 100.00% of diesel engines, 95.91% of pumps, 94.29% of coolers, 98.54% of oil separators, 64.21% of meters, 60.23% of reservoirs, and 75.32% of valves in the actual marine engine room.

Cyber security has drawn more attention in the research of intelligent and unmanned ships. The remote control command transmitted in time sequence has a high similarity. This makes the ships more vulnerable to cyber-attacks, especially when they are controlled remotely. Aiming at the defense of Differential Fault Attack (DFA), this paper improved the SM4 algorithm in the phase of the S-box generation and circular encryption. The Wasserstein GAN Gradient Penalty (WGAN-GP) is used to generate S-boxes dynamically to confuse differential distribution tables. After the round encryption, the combination transformation is further applied to prevent from DFA. The corresponding symmetric decryption algorithm is also developed. Simulation result shows that the generated S-box meets the cryptography criteria and the combined transformation effectively hides the sensitive information in output ciphertext and guards against the DFA.