摘要
针对现有语义分割方法存在分割速度慢、精度低等不足,通过结合MobileNet模块、深度可分离卷积DSC以及CBAM注意力机制,提出一种基于改进的DeepLabv3+网络的苹果图像分割方法,可用于苹果自动化采摘工作中苹果果实的定位。通过实验探究腐坏程度、腐坏位置、苹果品种以及复杂背景等因素对苹果图像语义分割性能的影响,同时验证该方法的鲁棒性。
In response to the shortcomings of existing semantic segmentation methods such as slow segmentation speed and low accuracy,an apple image segmentation method based on an improved DeepLabv3plus network is proposed by combining the MobileNet module,deep separable convolutional DSC,and CBAM attention mechanism,which can be used for apple fruit positioning in automated apple picking work.Through experiments,the effects of factors such as degree of decay,location of decay,apple variety,and complex background on the semantic segmentation performance of apple images were explored,and the robustness of this method was verified.
出处
《智慧农业导刊》
2023年第16期5-10,15,共7页
JOURNAL OF SMART AGRICULTURE
关键词
苹果图像
农作物语义分割
自动采摘
果实定位
注意力机制
apple image
crop semantic segmentation
automatic picking
fruit positioning
attention mechanism
