High-intensive underground mining has caused severe ground fissures,resulting in environmental degradation.Consequently,prompt detection is crucial to mitigate their environmental impact.However,the accurate segmentat...High-intensive underground mining has caused severe ground fissures,resulting in environmental degradation.Consequently,prompt detection is crucial to mitigate their environmental impact.However,the accurate segmentation of fissuresin complex and variable scenes of visible imagery is a challenging issue.Our method,DeepFissureNets-Infrared-Visible(DFN-IV),highlights the potential of incorporating visible images with infrared information for improved ground fissuresegmentation.DFNIV adopts a two-step process.First,a fusion network is trained with the dual adversarial learning strategy fuses infrared and visible imaging,providing an integrated representation of fissuretargets that combines the structural information with the textual details.Second,the fused images are processed by a fine-tunedsegmentation network,which lever-ages knowledge injection to learn the distinctive characteristics of fissuretargets effectively.Furthermore,an infrared-visible ground fissuredataset(IVGF)is built from an aerial investigation of the Daliuta Coal Mine.Extensive experiments reveal that our approach provides superior accuracy over single-modality image strategies employed in fivesegmentation models.Notably,DeeplabV3+tested with DFN-IV improves by 9.7%and 11.13%in pixel accuracy and Intersection over Union(IoU),respectively,compared to solely visible images.Moreover,our method surpasses six state-of-the-art image fusion methods,achieving a 5.28%improvement in pixel accuracy and a 1.57%increase in IoU,respectively,compared to the second-best effective method.In addition,ablation studies further validate the significanceof the dual adversarial learning module and the integrated knowledge injection strategy.By leveraging DFN-IV,we aim to quantify the impacts of mining-induced ground fissures,facilitating the implementation of intelligent safety measures.展开更多
基金supported by the National Science Fund of China(Grant No.52225402)Fund of Inner Mongolia Research Institute,China University of Mining and Technology(Beijing)(Grant No.IMRI23003).
文摘High-intensive underground mining has caused severe ground fissures,resulting in environmental degradation.Consequently,prompt detection is crucial to mitigate their environmental impact.However,the accurate segmentation of fissuresin complex and variable scenes of visible imagery is a challenging issue.Our method,DeepFissureNets-Infrared-Visible(DFN-IV),highlights the potential of incorporating visible images with infrared information for improved ground fissuresegmentation.DFNIV adopts a two-step process.First,a fusion network is trained with the dual adversarial learning strategy fuses infrared and visible imaging,providing an integrated representation of fissuretargets that combines the structural information with the textual details.Second,the fused images are processed by a fine-tunedsegmentation network,which lever-ages knowledge injection to learn the distinctive characteristics of fissuretargets effectively.Furthermore,an infrared-visible ground fissuredataset(IVGF)is built from an aerial investigation of the Daliuta Coal Mine.Extensive experiments reveal that our approach provides superior accuracy over single-modality image strategies employed in fivesegmentation models.Notably,DeeplabV3+tested with DFN-IV improves by 9.7%and 11.13%in pixel accuracy and Intersection over Union(IoU),respectively,compared to solely visible images.Moreover,our method surpasses six state-of-the-art image fusion methods,achieving a 5.28%improvement in pixel accuracy and a 1.57%increase in IoU,respectively,compared to the second-best effective method.In addition,ablation studies further validate the significanceof the dual adversarial learning module and the integrated knowledge injection strategy.By leveraging DFN-IV,we aim to quantify the impacts of mining-induced ground fissures,facilitating the implementation of intelligent safety measures.
