The evaluation of grassland degradation is an important part of ecological conservation research, and rodent infestation is a significant factor in grassland degradation. The presence of a large number of mouseholes means that the environmental balance of grassland has been destroyed, so the coverage of mouseholes can be used as an evaluation method for grassland degradation levels. In this paper, the image segmentation method is used to segment the mousehole images, Upernet is used as the segmentation network, and Swin Transformer as the Backbone. FAM and FSM modules are added to the Upernet network to solve the target misalignment problem when upsampling the network. The mIoU is improved by 5.3% according to the experimental results.
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.
Studies by grassland workers have shown that the occurrence of degradation indicator plants in grassland is an important sign of grassland degradation. The detection of degradation indicator plants can provide a certain data basis for the study of grassland degradation. In this paper, a target detection algorithm for improving YOLOv5 model is proposed to detect the degradation indicator plants (wolfsbane) in grassland. Firstly, the target detection dataset of the grassland degradation indicator plant(wolfsbane)is constructed, and then the backbone network is optimized by adding a coordinate attention mechanism on the basis of the original YOLOv5 model; The original feature pyramid module in the feature fusion module is replaced by a weighted bidirectional feature pyramid (BiFPN) network structure, which realizes effective weighted feature fusion and bi-directional cross-scale connection; A small target detection layer is also added to further improve the detection accuracy of small targets. Experimental results show that the proposed improved algorithm achieves an average precision (AP) of 80.4%, which is 3.4% better than the original YOLOv5 model, and verifies the effectiveness of the improved model for the detection of degraded indicator plants (wolfsbane).
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