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.
Keywords: YOLOv5; intelligent ship; model improvement; object detection; visual recognition.