Roadside object detection algorithm with multi-scale feature fusion and interaction
In view of the challenges of dense small targets,multi-scale variations,and complex weather background interference in roadside perspective target detection tasks,a multi-scale feature fusion and interaction-based target detection algorithm,MF-YOLO,is proposed.Design C2f-CAST,interact and transform features from different subspaces through star operations,and introduce MLCA to capture local,global,channel,and spatial features between distant pixels.Multi-scale information aggregation enhances attention to significant semantic information of occluded objects and eliminates background influence;to address the problem of low efficiency in context information fusion for the neck layer,we add lightweight convolution GSConv to optimize traditional convolution,and design a cross-level partial network module VoV-GSCSP to reduce model complexity and parameter count.Construct a cross-level fusion module SDFM to perform self-calibration on shallow feature maps and fuse semantic information from deep feature maps to solve the problem of missed detection of small targets;finally,the design is based on an adaptive penalty factor,a gradient adjustment function for anchor box quality combined with a dynamic clustering mechanism to improve the WPIoU loss function,enhancing the performance of bounding box regression and detection robustness.The experimental results show that MF-YOLO achieves mAP@0.5 of 85.1%and 92.3%on DAIR-V2X-I and UA-DETRAC datasets,respectively,which is 4.4%and 1.8%higher than the original YOLOv8s,with a reduction of 19.8%in computational complexity and 8.18%in parameter count.The detection speed reaches 152 fps,meeting the real-time requirements.
万方数据
维普
