Research on Ship-Engine-Room-Equipment Detection Based on Deep Learning

The visual monitoring of ship-engine-room equipment is an essential component of ship-cabin intelligence. In response to issues such as imbalanced quantities of different categories of engine room equipment and severe occlusion, this paper presents improvements to YOLOv8-M. Firstly, the introduction of the SPPFCSPC module enhances the feature extraction capabilities of the backbone extraction network. Subsequently, improvements are implemented in the neck network to create GCFPN, facilitating further feature fusion, and introducing the Dynamic Head module, which fuses the deformable convolution, in the part of the detection head, so as to improve the performance of the network. Finally, the FOCAL EIOU LOSS is introduced, while mitigating the impact of dataset imbalance through class-wise data augmentation. In this paper, the ship cabin equipment dataset and the public dataset MS COCO2017 are evaluated. Compared with YOLOv8-M, the mAP50 of GCD-YOLOv8 is improved by 2.6% and 0.4%, respectively.