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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%.
