# Research on Improved Lightweight YOLOv5s for Multi-Scale Ship Target Detection
## 5 Conclusions  
This study proposes a lightweight multi-scale ship target detection model, Yolov5s_ESGW, based on the Yolov5 model, which can be used for ship target recognition in various scenarios such as fog, occlusion, and those involving small targets. The main improvements of Yolov5s_ESGW can be divided into three categories. First, the width and depth of the model are improved based on the lightweight Efficient-NetV2 network and Ghost module, respectively. The main purpose is to compress the calculation amount and parameter amount of the model as much as possible and improve the detection speed of the model; secondly, a lightweight attention module is embedded after the C3 Ghost module to improve the feature expression ability of the model; finally, Wise-IoU calculation is used as the bounding box loss to improve the detection performance and generalization ability of the model. The performance of the Yolov5s_ESGW model is trained and tested on the collected and produced MSD dataset. The test results show that the Yolov5s_ESGW model proposed in this study is better than the original Yolov5s model, with the number of parameters, GFLOPs value, and model file size decreasing by 42.82%, 68.35%, and 41.46%, respectively. The mAP0.5 value of the improved model has increased by 0.001, the inference speed has increased by 29.58%, and the detection accuracy (P) is basically the same as the original Yolov5s model. Compared with other mainstream lightweight detection models, this model has the advantages of high detection accuracy, a small amount of calculation and parameters, and fast inference speed.  
However, it is undeniable that the dataset used in this study contains relatively few targets, types, and scenarios and cannot fully cover all ship navigation scenarios. In addition, this technology has not been combined with other technologies to comprehensively improve ship driving performance, such as the integration of radar, AIS, and image detection data. In the future, the dataset needs to be further improved to increase the diversity of data. Based on this research result, combined with advanced sensors, further in-depth research will be conducted on ship BEV technology and visual navigation technology to achieve reconstruction of complex navigation situations. In practical applications, this technology can improve the navigation performance of ships, enhance the anti-collision warning capability of ships, enrich the means of maritime traffic control, and promote the safe operation of ships. In addition, this research result shows a low demand for hardware computing power, which can reduce the cost of deploying this technology on the ship side in the future, and it has certain economic value and application prospects.  
Conceptualization, P.Z. ([PERSON]); data curation, [PERSON] ([PERSON]) and W.G.; formal analysis, [PERSON] ([PERSON]), P.Z. ([PERSON]) and J.Z.; funding acquisition, Z.S.; investigation, Z.S. and W.G.; methodology, P.Z. ([PERSON]); project administration, Z.S. and J.D. ([PERSON]); software, P.Z. ([PERSON]); supervision, J.D. ([PERSON]); validation, P.Z. ([PERSON]), J.D. ([PERSON]) and J.D ([PERSON]); visualization, P.Z. ([PERSON]), J.Z. and W.G.; writing--original draft, P.Z. ([PERSON]); writing--review and editing, J.Z. All authors have read and agreed to the published version of the manuscript.  
The work was supported by The Development and Application Project of Ship CAE Software (2024-2026).  
Not applicable.  
The data presented in this study are available in the article. For further inquiries, please contact the corresponding author.  
The authors declare no conflicts of interest.