Based on the Improved YOLOv8 Crankshaft Surface Defect Detection Algorithm
A proposed improved method for the detection of crankshaft RB-YOLOv8 surface defects was presented,due to the complexity and difficulty posed by complex defect backgrounds as well as the slow rate at which small target flaws can be detected.To begin,the C2f module of the classic backbone network is sup-planted by RepViT.The lightweight model is brought in to decrease the computational intricacy of the net-work and enhance detection velocity.Additionally,BiFPN bidirectional feature fusion module is advanced and a Small target detection layer is incorporated,thereby augmenting the detection capability of minor target flaws.Introducing the BiFormer attention mechanism to bolster the model's robustness and enhance its detec-tion accuracy in order to address complex defect backgrounds,MPDIoU loss function was then utilized for further enhancement.The experiment results demonstrate that the mAP value of the three defective samples is 98.4%.the number of model parameters is reduced to 2.797 MB,and the FPS is increased to 169 f/s,which can accurately detect the surface defects of the crankshaft,and has practical application value.
crankshaft surface defect detectionRepViT networkBiFPN moduleBiFormer attention mechanismMPDIoU loss
万方数据
维普
