  The traditional SegNet architecture commonly encounters significant
information loss during the sampling process, which detrimentally affects its
accuracy in image semantic segmentation tasks. To counter this challenge, we
introduce an innovative encoder-decoder network structure enhanced with
residual connections. Our approach employs a multi-residual connection strategy
designed to preserve the intricate details across various image scales more
effectively, thus minimizing the information loss inherent to down-sampling
procedures. Additionally, to enhance the convergence rate of network training
and mitigate sample imbalance issues, we have devised a modified cross-entropy
loss function incorporating a balancing factor. This modification optimizes the
distribution between positive and negative samples, thus improving the
efficiency of model training. Experimental evaluations of our model demonstrate
a substantial reduction in information loss and improved accuracy in semantic
segmentation. Notably, our proposed network architecture demonstrates a
substantial improvement in the finely annotated mean Intersection over Union
(mIoU) on the dataset compared to the conventional SegNet. The proposed network
structure not only reduces operational costs by decreasing manual inspection
needs but also scales up the deployment of AI-driven image analysis across
different sectors.
