LAD‐YOLOv8n: A Novel Model for Rare Earth Molten Salt Flame Detection

ABSTRACT

Accurate detection of the neodymium rare‐earth molten salt flame is critical for automating neodymium metal production, as it directly determines the addition of neodymium oxide raw material. Thus, this study proposes LAD‐YOLOv8n, an enhanced YOLOv8‐based model tailored for neodymium rare‐earth molten salt flame detection. The method incorporates an image pre‐processing strategy based on a chromaticity‐space transformation, constructing a colour model tailored to the burning characteristics of molten salt to accurately extract its regions. With YOLOv8n serving as the backbone, the model integrates the asymptotic feature pyramid network for multi‐scale feature fusion, the large separable kernel attention mechanism for shape‐aware focus, and the DySample upsampler for enhanced flame localization. These modifications effectively mitigate challenges such as flame variability and background noise, thereby significantly enhancing the robustness of the detection process. Ablation experiments validate the individual contributions of each component and demonstrate their synergistic effects. Comparative assessments against SVM, LSTM, ResNet‐50, RT‐DETR‐l and other YOLO variants reveal that LAD‐YOLOv8n achieves a mean average precision of 77.0%—a 3.6% improvement over the original YOLOv8n. This confirms that the proposed deep learning recognition algorithm is a viable and effective approach for detecting rare‐earth molten‐salt flames.