Tool semantic segmentation algorithm based on improved BiSeNetV2
In addressing the automated detection task for maintenance tools in the generator wind tunnel scenario,this study proposes an enhanced semantic segmentation algorithm based on BiSeNetV2.The improvement involves introducing the SPPFCSPC module at the end of the detail branch to fuse features from different scales.Additionally,standard convolutions in the detail branch are replaced with depthwise separable convolution,resulting in a significant reduction in computational complexity.To optimize feature extraction performance,an efficient channel attention module is incorporated into the semantic branch,enhanc-ing segmentation accuracy.Experimental results demonstrate that the enhanced BiSeNetV2 algorithm accurately detects the posi-tion information of maintenance tools.Compared to the original BiSeNetV2,this algorithm exhibits a 3.7 percentage improvement in MIoU and a 16.4%improvement in inference speed.
NETL
NSTL
万方数据
