Objectives The standardization of ship engine room operations is a critical component of ship safety management. Therefore, the practical examination for crew members includes the disassembly and assembly of ship equipment as a key assessment item. To enhance the digitalization and intelligence of crew practical examinations, a computer vision-based automated recognition method for assessing the standardization of ship equipment disassembly and assembly processes is proposed.

Methods First, the backbone network of the ship equipment detection model is constructed using YOLOv8n, and the shuffle-attention (SA) mechanism is introduced to improve the model's feature extraction capability and training efficiency. Subsequently, a reparameterized generalized feature pyramid network (GFPN) fusion structure is incorporated into the neck network to enhance the model's ability to fuse multi-scale features. Finally, the original CIoU loss function is replaced with the wise intersection over union (Wise-IoU) loss function to improve the model's accuracy.

Results Experimental results on a self-constructed dataset demonstrate that, compared to YOLOv8n, the improved object detection algorithm achieves a 0.15 increase in mean average precision and a 0.6 frames-per-second improvement in real-time detection, enabling accurate recognition of the disassembly and assembly processes of gear pumps.

Conclusions The improved algorithm exhibits superior recognition capabilities and is better suited for identifying the standardization of ship equipment disassembly and assembly processes.