Research on gangue target detection algorithm based on MBI-YOLOv8
To achieve a balance between detection performance and resource consumption in the gangue sorting domain,an efficient,real-time,lightweight object detection algorithm based on an improved YOLOv8 was proposed,suitable for low-performance detection platforms.This algorithm built on the YOLOv8n architecture and incorporated MobileNetv3 to replace the original backbone network,leveraging its lightweight structure to reduce model parameters and computational load,thereby enhancing detection speed.Additionally,the algorithm integrated the BIFPN module for feature enhancement,which employed multi-scale feature fusion to compensate for the loss of detection accuracy associated with the lightweight network,thus achieving model lightweighting while maintaining detection accuracy.Furthermore,the Inner-CIoU bounding box regression loss function was introduced to balance the training results of images with varying qualities,improving the model's localization capability and further enhancing detection accuracy and speed.To validate the effectiveness of the proposed algorithm,experiments were conducted to compare it with YOLOv3-tiny,YOLOv5n,YOLOv7,and YOLOv8n on a custom dataset.Experimental results demonstrated that the proposed algorithm exhibited optimal overall detection performance.While maintaining detection accuracy,the model's parameter count was reduced to 1,188,725,representing a 60.46%decrease compared to YOLOv8n.The computational load was reduced from 8.1 GFLOPs to 2.8 GFLOPs,and the FPS increased from 86.02 Hz to 216.58 Hz.This research indicated that the proposed algorithm is a highly efficient,real-time,lightweight gangue detection method with significant potential and advantages in balancing detection performance and computational resource consumption.
万方数据
维普
