The construction industry is accident-prone, and unsafe behaviors of construction workers have been identified as a leading cause of accidents. One important countermeasure to prevent accidents is monitoring and managing those unsafe behaviors. The most popular way of detecting and identifying workers’ unsafe behaviors is the computer vision-based intelligent monitoring system. However, most of the existing research or products focused only on the workers’ behaviors (i.e., motions) recognition, limited studies considered the interaction between man-machine, man-material or man-environments. Those interactions are very important for judging whether the workers’ behaviors are safe or not, from the standpoint of safety management. This study aims to develop a new method of identifying construction workers’ unsafe behaviors, i.e., unsafe interaction between man-machine/material, based on ST-GCN (Spatial Temporal Graph Convolutional Networks) and YOLO (You Only Look Once), which could provide more direct and valuable information for safety management. In this study, two trained YOLO-based models were, respectively, used to detect safety signs in the workplace, and objects that interacted with workers. Then, an ST-GCN model was trained to detect and identify workers’ behaviors. Lastly, a decision algorithm was developed considering interactions between man-machine/material, based on YOLO and ST-GCN results. Results show good performance of the developed method, compared to only using ST-GCN, the accuracy was significantly improved from 51.79% to 85.71%, 61.61% to 99.11%, and 58.04% to 100.00%, respectively, in the identification of the following three kinds of behaviors, throwing (throwing hammer, throwing bottle), operating (turning on switch, putting bottle), and crossing (crossing railing and crossing obstacle). The findings of the study have some practical implications for safety management, especially workers’ behavior monitoring and management.

Study on the Interaction Behaviors Identification of Construction Workers Based on ST-GCN and YOLO

pdf/research-on-the-recognition-method-of-unsafe-action-in-sq579dx3mvds.pdf

Research on the Recognition Method of Unsafe Action in Mooring Operations Based on Improved YOLOv7

Officers on Watch (OOWs) of the ship’s bridge play a vital role in maritime navigation safety, monitoring the ship’s navigational status, and ensuring maritime safety. The status of inactive watch officers, such as fatigued driving and negligence on lookout, is one of the main causes of accidents. Intelligent technology for real-time perception and state evaluation of ship OOWs significantly reduces accidents caused by human factors. The traditional computer vision method is difficult to adapt to the complex environment of a ship bridge, and carries strong privacy risks. With the development of Internet of Things technology, sensing technology based on ubiquitous Wi-Fi devices provides a new way to accurately monitor the status of ship OOWs. In this paper, we use commercial off-the-shelf (COTS) Wi-Fi devices to propose a ship driving activity state detection method based on beamforming feedback information (BFI). Using wireless sensing data to sense the number of OOWs and their driving behavior realizes low-cost and high-precision detection of the behavioral status of the ship’s bridge watchkeeper. Experiments were conducted in a ship-driving simulation laboratory and on a real-world Yangtze River cruise ship. The experimental results demonstrate that our proposed method achieves 92.4% and 98.1% accuracy for tracking active status and estimating the number of OOWs, respectively.

Ship Bridge OOW Activity Status Detection Using Wi-Fi Beamforming Feedback Information

Abstract The primary objective of this research was to implement a more efficient handling strategy in the container terminal storage yard at the Baltic Hub Container Terminal (BHCT). Following an analysis of the current logistics system associated with starboard-side mooring, an attempt was made to shift the mooring to the port side. Two routing strategies corresponding to the analysed mooring operations were tested using a developed model. Simulations were conducted to enable a comparison of various handling strategies that were suitable for the mooring arrangements and equipment under investigation. This analysis was based on reliable information obtained from a real process. Evaluative criteria for examining the impact of changing the handling strategy of internal movement vehicles (IMVs) in the terminal storage yard in terms of process efficiency included the total time and distance of container transportation and the truck utilisation level. A new route for IMVs was developed to accommodate the changed mooring operation. This adjustment aimed to enhance the discharge process and to reduce the distance travelled, resulting in a reduction in fuel consumption. The shorter travel distance also positively impacted productivity, and contributed to a reduction in operating costs. The changes to the mooring arrangement directly resulted in a 10% increase in the quay crane gross productivity, measured in moves per hour (mph). Given that many ports do not pay attention to how ships are moored, the case study and analysis presented here can help staff quickly improve their handling strategy and productivity, thus increasing the port’s competitiveness. 

Simulation-Based Approach to a More Efficient Handling Strategy for a Container Terminal Storage Yard: Case Study of the Baltic Hub Container Terminal

Mooring (Thin) lines are fabricated of polyester ropes, steel wire ropes, and chains. These are considered the essential components which are used to secure offshore marine vessels and floating facilities by keeping them in a fixed place and resisting external loads. However, the failure of any mooring lines because of anomalies can cause severe consequences including financial losses, loss of life, and harm to the environment. Thus, it is essential to determine the anomalies in mooring lines beforehand to ascertain reliable and safe offshore mooring operations. This paper furnishes a comprehensive review of various types of anomalies in mooring lines with their underlying causes, and risk mitigation tactics. Furthermore, the types of mooring lines including polyester ropes, chain, and steel wire ropes have been discussed with their advantages and disadvantages. Additionally, the real-time consequences of failure in mooring lines are explored which occur due to the anomalies in the mooring lines including but not limited to environmental damage, vessel drift, and collision. In order to reduce the risks associated with mooring line anomalies, this review concludes by summarizing the major findings and emphasizing the significance of proactive monitoring and maintenance. 

Anomalies in Mooring (Thin) Lines: Causes, Risk Mitigations, and Real Time Consequences of Failure – A Comprehensive Review

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.

https://www.mdpi.com/2077-1312/11/2/291/pdf?version=1675085527

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

Ninety percent of the world’s cargo is transported by sea, and the fatigue of ship officers of the watch (OOWs) contributes significantly to maritime accidents. The fatigue detection of ship OOWs is more difficult than that of vehicle drivers due to an increase in the automation degree. In this study, research progress pertaining to fatigue detection in OOWs is comprehensively analyzed based on a comparison with that in vehicle drivers. Fatigue detection techniques for OOWs are organized based on input sources, which include the physiological/behavioral features of OOWs, vehicle/ship features, and their comprehensive features. Prerequisites for detecting fatigue in OOWs are summarized. Subsequently, various input features applicable and existing applications to the fatigue detection of OOWs are proposed, and their limitations are analyzed. The results show that the reliability of the acquired feature data is insufficient for detecting fatigue in OOWs, as well as a nonnegligible invasive effect on OOWs. Hence, low-invasive physiological information pertaining to the OOWs, behavior videos, and multisource feature data of ship characteristics should be used as inputs in future studies to realize quantitative, accurate, and real-time fatigue detections in OOWs on actual ships.

Fatigue Detection for Ship OOWs Based on Input Data Features, From the Perspective of Comparison With Vehicle Drivers: A Review

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Xue Yang

Author Tools

Chat about Author

Papers

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

Fatigue Detection for Ship OOWs Based on Input Data Features, From the Perspective of Comparison With Vehicle Drivers: A Review