Design and application of the foreign object detection algorithm for engine cylinder based on YOLOv8
To solve the problems of missed and false detection when detecting foreign objects in engine cylinders by manual testing,an engine cylinder foreign object detection algorithm based on the improved object detection algorithm YOLOv8 is designed and experimentally verified.Based on the attention mechanism in CoTNet,a Contextual Attention module and reconstruct the Bottleneck structure in C2f,named CoA_C2f,are designed to replace the C2f module in the YOLOv8 backbone network.In the Neck section of the model,the continuously upsampled feature map Concat module is replaced with the context aggregation module CAM.Triplet Attention module is embed between Neck and Head.The experimental results show that the designed engine cylinder foreign object detection model can effectively identify foreign objects in the cylinder,and the average detection accuracy is improved by 21.65%after introducing CoA_C2f,CAM,and Triplet Attention modules on the basis of the original YOLOv8s.
万方数据
维普
