Scenario recognition and tracking for cargo handling operations in autonomous and non-sparse outdoor industrial environments
Abstrak
This paper presents a comprehensive study on the application of computer vision technologies for scenario recognition and tracking in industrial cargo handling operations, particularly within non-sparse and autonomous outdoor environments. We introduce a novel dataset and methodology aimed at real-time identification of various elements including personnel, containers, cages, equipment, boxes, and piping. Using the YOLOv8 neural network, our experiments achieved high accuracy, with precision reaching up to 87.3%. The model demonstrated effective detection and segmentation capabilities even in complex, non-sparse environments. These results suggest a significant enhancement in the decision-making processes and accident prevention strategies within industrial operations, underscoring the potential of advanced computer vision systems in improving safety and operational efficiency.
Topik & Kata Kunci
Penulis (5)
J. V. D. Santos
Guilherme Silva
E. Borges
P. Drews
Silvia Silva da Costa Botelho
Akses Cepat
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- 2024
- Bahasa
- en
- Sumber Database
- Semantic Scholar
- DOI
- 10.1109/IECON55916.2024.10905130
- Akses
- Open Access ✓