LC-DETR-Based Target Detection Algorithm for Separating Point of Ore Belt in Spiral Concentrator
Huizhong Liu, Jun Hu, Ruoyun Zhang, Yunchen Zhang, Fuquan Liu, Yang Liao · 2025
The key to utilising the intelligence of a spiral concentrator, a type of gravity beneficiation apparatus, is identifying the mineral zones in its sorting fluid. A form of LCDETR based on the enhanced RT-DETR algorithm is proposed for detecting the separation point of the spiral concentrator mineral zone in order to address the issue of complex backgrounds and fuzzy targets that are challenging to detect when detecting the target of the separation point of the spiral concentrator mineral zone. First, the HDRAB module and CSP architecture are combined with the HDCSPNet backbone network to enhance the model's detection accuracy in a high-noise environment. The second is a novel network architecture called BIMAFPN, which combines the MAFPN and BIFPN networks to enhance the model's anti-interference capabilities. To decrease the number of parameters in the approach and increase computational efficiency, the VoVGSCSP module and GSConv lightweight convolution are introduced. The model's capacity to handle the target specifics of the ore belt separation point in the spiral concentrator is essentially improved when the EUCB module finally realises the dimensionality and resolution adjustment of the stage feature map. The accuracy of the LC-DETR model is 90.5 %, according to experiments, which is 6 % to 10 % better than that of the conventional model. In comparison to the YOLO series algorithms, Faster R-CNN, and RetinaNet models, it meets industrial real-time requirements and has optimal detection accuracy and stability. Furthermore, the accuracy of LC-DETR remains consistent at roughly 90 % in a variety of complex scenarios, including those involving noise, fuzzy mineral zones, and varying lighting. This indicates a strong capacity for generalisation, which can reliably locate and identify the target, lessen the occurrence of leakage and misdetection, and meet the need for automatic identification of mineral zoning features in factory spiral concentrators.