Transportation Research Record Journal of the Transportation Research Board· 2026Q2
Fuzzy Logic and Object Identification of Wharf Yard Construction Mechanical Injury Risk Monitoring and Warning
- 0citations
- Q2SCImago
- 2026year
Short summary
A new system integrates fuzzy logic with YOLOv5 and Deep SORT object recognition to monitor and warn about mechanical injury risks in complex wharf yard construction environments, outperforming existing methods like TOPSIS and YOLOv3.
AI-generated from the title and abstract; the full text is not read.
Key points
- Proposes a novel risk monitoring and early warning system for wharf yard construction.
- Integrates fuzzy inference for risk quantification with YOLOv5 and Deep SORT for object recognition and tracking.
- Addresses limitations of existing methods (TOPSIS, YOLOv3) in accuracy and real-time performance.
- Delivers real-time early warnings and countermeasure recommendations.
AI-generated from the title and abstract; the full text is not read.
Abstract
The construction environment of wharf yards is highly complex, with significant risks of mechanical injuries, underscoring the need for effective monitoring and early warning systems. Existing methods, such as the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and You Only Look Once version 3 (YOLOv3), often suffer from limited accuracy and poor real-time performance, making them unsuitable for dynamic conditions. To address this gap, this study proposes a risk monitoring and early warning approach that integrates fuzzy inference with target recognition. Fuzzy inference was used to quantify and classify mechanical injury risks, while the You Only Look Once version 5 (YOLOv5) detection algorithm and Simple Online and Realtime Tracking with a Deep Association Metric (Deep SORT) technique enabled accurate recognition and tracking of workers and machinery. By combining fuzzy logic–based risk levels with target recognition outputs, the system delivers real-time early warnings and countermeasure recommendations, providing a robust solution for enhancing human–machine safety in dynamic construction environments.
The authors' abstract, as published at the source. Transportation Research Record Journal of the Transportation Research Board, 2026 · DOI ↗
Continue with a free account
Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.
Continue free on the webSign in with Google or Apple; no card needed. You come back to this paper.
On your phone:
Field: Radiological and Ultrasound Technology
Radiological and Ultrasound TechnologyHealth Professions