Recognition of Unsafe Onboard Mooring and Unmooring Operation Behavior Based on Improved YOLO-v4 Algorithm

Recognition of Unsafe Onboard Mooring and Unmooring Operation Behavior Based on Improved YOLO-v4 Algorithm

Zhao, Changjiu (Dalian Maritime University; Dalian University)
Zhang, Wenjun (Dalian Maritime University; Dalian University)
Chen, C.Y. (TU Delft Transport Engineering and Logistics)
Yang, Xue (Dalian Maritime University; Dalian University)
Yue, Jingwen (Dalian Maritime University; Dalian University)
Han, Bing (Shanghai Ship and Shipping Research Institute Co.; Minjiang University)

2023

In the maritime industry, unsafe behaviors exhibited by crew members are a significant factor contributing to shipping and occupational accidents. Among these behaviors, unsafe operation of mooring lines is particularly prone to causing severe accidents. Video-based monitoring has been demonstrated as an effective means of detecting these unsafe behaviors in real time and providing early warning to crew members. To this end, this paper presents a dataset comprising videos of unsafe mooring line operations by crew members on the M.V. YuKun. Additionally, we propose an unsafe behavior recognition model based on the improved You Only Look Once (YOLO)-v4 network. Experimental results indicate that the proposed model, when compared to other models such as the original YOLO-v4 and YOLO-v3, demonstrates a significant improvement in recognition speed by approximately 35% while maintaining accuracy. Additionally, it also results in a reduction in computation burden. Furthermore, the proposed model was successfully applied to an actual ship test, which further verifies its effectiveness in recognizing unsafe mooring operation behaviors. Results of the actual ship test highlight that the proposed model’s recognition accuracy is on par with that of the original YOLO-v4 network but shows an improvement in processing speed by 50% and a reduction in processing complexity by about 96%. Hence, this work demonstrates that the proposed dataset and improved YOLO-v4 network can effectively detect unsafe mooring operation behaviors and potentially enhance the safety of marine operations.

maritime safety
Mobilenet-v3
unsafe behavior
YOLO-v4

http://resolver.tudelft.nl/uuid:408eae9f-2fc9-49f5-8fda-43635d0abd16

https://doi.org/10.3390/jmse11020291

2077-1312

Journal of Marine Science and Engineering, 11 (2)

Institutional Repository

journal article

© 2023 Changjiu Zhao, Wenjun Zhang, C.Y. Chen, Xue Yang, Jingwen Yue, Bing Han