A landslide is one of the most common geological disasters, which is associated with great destructive power and harm.In recent years, semantic segmentation models have been applied to landslide recognition research and have made some achievements.However, the current method still has issues, overlooking small targets like fine cracks, missegmenting boundaries, and struggling to differentiate spectral signatures such fp9550bk as those of different rock types in landslide-prone areas.In this paper, a landslide detection model based on the DeepLabv3+ framework, DeepLabv3+-ResNet101-ECA, is proposed.
The backbone feature extraction network of DeepLabv3+ is replaced with ResNet101 to enhance the feature extraction ability of the model for small objects.The ECA attention mechanism is integrated into the model to improve the accuracy of the object segmentation and improve the detection canon imageclass mf227dw accuracy.Taking the landslide in Bijie City, Guizhou Province, as the research object, compared with the original DeepLabv3+ model, the precision of DeepLabv3+-ResNet101-ECA is increased by 1.17%, the recall rate is increased by 2%, the F1 score is increased by 0.
96%, and the MIou is increased by 2.36%.Finally, transfer learning is used to verify the generalization ability of the model.The results show that the improved model has a better detection effect on landslides.