YOLOv8n-Al-Dehazing: A Robust Multi-Functional Operation Terminals Detection for Large Crane in Metallurgical Complex Dust Environment
In the aluminum electrolysis production workshop, heavy-load overhead cranes equipped with multi-functional operation terminals are responsible for critical tasks such as anode replacement, shell breaking, slag removal, and material feeding. The real-time monitoring of these four types of operation...
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MDPI AG
2025-03-01
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| author | Yifeng Pan Yonghong Long Xin Li Yejing Cai |
| author_facet | Yifeng Pan Yonghong Long Xin Li Yejing Cai |
| author_sort | Yifeng Pan |
| collection | DOAJ |
| description | In the aluminum electrolysis production workshop, heavy-load overhead cranes equipped with multi-functional operation terminals are responsible for critical tasks such as anode replacement, shell breaking, slag removal, and material feeding. The real-time monitoring of these four types of operation terminals is of the utmost importance for ensuring production safety. High-resolution cameras are used to capture dynamic scenes of operation. However, the terminals undergo morphological changes and rotations in three-dimensional space according to task requirements during operations, lacking rotational invariance. This factor complicates the detection and recognition of multi-form targets in 3D environment. Additionally, operations like striking and material feeding generate significant dust, often visually obscuring the terminal targets. The challenge of real-time multi-form object detection in high-resolution images affected by smoke and dust environments demands detection and dehazing algorithms. To address these issues, we propose the YOLOv8n-Al-Dehazing method, which achieves the precise detection of multi-functional material handling terminals in aluminum electrolysis workshops. To overcome the heavy computational costs associated with processing high-resolution images by using YOLOv8n, our method refines YOLOv8n through component substitution and integrates real-time dehazing preprocessing for high-resolution images, thereby reducing the image processing time. We collected on-site data to construct a dataset for experimental validation. Compared with the YOLOv8n method, our method approach increases inference speed by 15.54%, achieving 120.4 frames per second, which meets the requirements for real-time detection on site. Furthermore, compared with state-of-the-art detection methods and variants of YOLO, YOLOv8n-Al-Dehazing demonstrates superior performance, attaining an accuracy rate of 91.0%. |
| format | Article |
| id | doaj-art-55532853a1c04eb49496b408dc5f8b2b |
| institution | Kabale University |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-03-01 |
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| spelling | doaj-art-55532853a1c04eb49496b408dc5f8b2b2025-08-20T03:43:36ZengMDPI AGInformation2078-24892025-03-0116322910.3390/info16030229YOLOv8n-Al-Dehazing: A Robust Multi-Functional Operation Terminals Detection for Large Crane in Metallurgical Complex Dust EnvironmentYifeng Pan0Yonghong Long1Xin Li2Yejing Cai3College of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, ChinaCollege of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, ChinaCollege of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, ChinaCollege of Railway Transportation, Hunan University of Technology, Zhuzhou 412007, ChinaIn the aluminum electrolysis production workshop, heavy-load overhead cranes equipped with multi-functional operation terminals are responsible for critical tasks such as anode replacement, shell breaking, slag removal, and material feeding. The real-time monitoring of these four types of operation terminals is of the utmost importance for ensuring production safety. High-resolution cameras are used to capture dynamic scenes of operation. However, the terminals undergo morphological changes and rotations in three-dimensional space according to task requirements during operations, lacking rotational invariance. This factor complicates the detection and recognition of multi-form targets in 3D environment. Additionally, operations like striking and material feeding generate significant dust, often visually obscuring the terminal targets. The challenge of real-time multi-form object detection in high-resolution images affected by smoke and dust environments demands detection and dehazing algorithms. To address these issues, we propose the YOLOv8n-Al-Dehazing method, which achieves the precise detection of multi-functional material handling terminals in aluminum electrolysis workshops. To overcome the heavy computational costs associated with processing high-resolution images by using YOLOv8n, our method refines YOLOv8n through component substitution and integrates real-time dehazing preprocessing for high-resolution images, thereby reducing the image processing time. We collected on-site data to construct a dataset for experimental validation. Compared with the YOLOv8n method, our method approach increases inference speed by 15.54%, achieving 120.4 frames per second, which meets the requirements for real-time detection on site. Furthermore, compared with state-of-the-art detection methods and variants of YOLO, YOLOv8n-Al-Dehazing demonstrates superior performance, attaining an accuracy rate of 91.0%.https://www.mdpi.com/2078-2489/16/3/229YOLOv8high resolutionobject detectiondust scenesdehazingmulti-functional operation terminals |
| spellingShingle | Yifeng Pan Yonghong Long Xin Li Yejing Cai YOLOv8n-Al-Dehazing: A Robust Multi-Functional Operation Terminals Detection for Large Crane in Metallurgical Complex Dust Environment Information YOLOv8 high resolution object detection dust scenes dehazing multi-functional operation terminals |
| title | YOLOv8n-Al-Dehazing: A Robust Multi-Functional Operation Terminals Detection for Large Crane in Metallurgical Complex Dust Environment |
| title_full | YOLOv8n-Al-Dehazing: A Robust Multi-Functional Operation Terminals Detection for Large Crane in Metallurgical Complex Dust Environment |
| title_fullStr | YOLOv8n-Al-Dehazing: A Robust Multi-Functional Operation Terminals Detection for Large Crane in Metallurgical Complex Dust Environment |
| title_full_unstemmed | YOLOv8n-Al-Dehazing: A Robust Multi-Functional Operation Terminals Detection for Large Crane in Metallurgical Complex Dust Environment |
| title_short | YOLOv8n-Al-Dehazing: A Robust Multi-Functional Operation Terminals Detection for Large Crane in Metallurgical Complex Dust Environment |
| title_sort | yolov8n al dehazing a robust multi functional operation terminals detection for large crane in metallurgical complex dust environment |
| topic | YOLOv8 high resolution object detection dust scenes dehazing multi-functional operation terminals |
| url | https://www.mdpi.com/2078-2489/16/3/229 |
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