Improving Deeplabv3+ for highland mouse holes segmentation scenarios

Abstract

The rodent infestation problem is currently one of the important factors in the degradation of grassland in the Sanjiangyuan area. We need to infer the degradation of grassland by the area of grassland being gnawed, and thus provide help for grassland restoration work. To this end we have designed a DeeplabV3+ based mouse infestation scene segmentation method. On the basis of Deeplabv3+, different backbone feature extraction networks are adopted, and attention mechanism is introduced into the backbone to improve the accuracy of feature extraction and solve the problem of sample imbalance in our self-made dataset. For the training and validation of this network, we used a self-developed photographed and produced dataset of the distribution of mouse holes in the grassland pastures of Haibei, Qinghai Province, which contains various features of plateau mouse infestation. The model improvement resulted in a significant reduction in the training time of Deeplabv3+ on this dataset, and a certain degree of improvement in segmentation accuracy.