Multi-Scale Feature Fusion Network for Object Recognition in Mountain Farmland Environments
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Abstract
Object recognition in mountainous field environment is a crucial part of intelligent operation. Since the diversity of crops, weeds and other targets in the mountainous field environment, as well as the complexity of scale changes, object recognition faces great challenges. In order to solve these problems, it is particularly critical to effectively fuse global and local multi-scale features. To this end, this paper proposes a three-branch parallel multi-scale feature fusion network (MFFNet) for object recognition in mountain and field environments. The MFFNet contains specially designed global and local feature extraction modules, which can capture the global and local feature information in the image in parallel. In addition, the MFFNet introduces a feature fusion strategy based on channel attention and spatial attention to effectively integrate global and local features with different semantic depths. The experimental results show that our proposed MFFNet is superior to the comparison method in multiple index results on the self-built mountain field environment image dataset.
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