This invention relates to the field of
computer vision technology, and particularly to a method and
system for multi-scale segmentation of tunnels with multiple defects in low-light environments. The method includes: constructing a
smoothing and
sharpening module to perform
noise reduction and
sharpening processing on the input image; improving EfficientNetV2 and using it as the
backbone network to extract features from the image; adding an adaptive
image enhancement S-curve during progressive downsampling to adaptively enhance the dark areas of the feature map and reduce the highlights; feeding the feature map into an improved
pyramid pooling module for multi-scale
feature extraction, with each scale's feature map undergoing
dimensionality reduction using a
Hilbert curve and then performing self-attention mechanism calculations to obtain spatial information from the image; fusing the multi-scale feature maps through channel attention and
upsampling to the original image size to obtain the defect segmentation map. This invention addresses the problem of low segmentation accuracy caused by limited
image quality generated by drones in dark tunnels by designing an adaptive S-curve enhancement curve and adding it to the segmentation network.