reviseThese results imply that IIL in MTL module plays an important role in generating better representation for final rail surface defect segmentation As we expect the IIL effectively delivers the in

These findings suggest that the Inter-Information Learning (IIL) in the Multi-Task Learning (MTL) module plays a crucial role in producing improved representations for the segmentation of rail surface defects. As anticipated, the IIL effectively facilitates the exchange of information between the edge sub-network and the segmentation sub-network within the MTL module, resulting in enhanced segmentation outcomes.