Improved rapid detection method for foreign object intrusion on railroads based on FasterNet and YOLOv8s
Railway track obstructions pose potential threats to train operation safety,with severe inci-dents possibly leading to derailments,overturns,and casualties.To address the challenge of achieving real-time detection on edge devices,where existing railway intrusion detection models often fail,this paper proposes an improved railway obstruction detection algorithm based on FasterNet and YOLOv8s.First,FasterNet,a network with fewer parameters,replaces the CSPDarkNet53 back-bone of YOLOv8s for feature extraction,reducing both the parameters and computational complexity.Second,inspired by partial convolution in FasterNet,a FasterBlock module is introduced to replace the C2f module in YOLOv8s'neck,enabling multi-scale feature fusion and further decreasing model parameters.Finally,to mitigate accuracy loss caused by model lightweighting,a redesigned BiFPN-A feature fusion structure is proposed.In this structure,Fusion operations replace Concat for tensor concatenation,achieving feature map fusion via FasterBlock and Fusion.Additionally,a parameter-free attention mechanism SimAM is integrated before each FasterBlock,ensuring that the lightweight model maintains robust detection accuracy.The results demonstrate that the improved model achieves a 60.89%reduction in size,a 61.8%decrease in parameters,and a 45.1%reduction in computational complexity,with only a 0.2%loss in detection accuracy.
万方数据
维普
