基于非对称混洗卷积神经网络的苹果叶部病害分割

针对苹果叶部病害由于数据集类间样本不均衡和拍摄角度、光照变化等实际成像与环境因素造成的精度低和泛化能力差的问题,本文提出了一种新型的非对称混洗卷积神经网络ASNet.首先,通过在ResNeXt骨干网络中添加改进的scSE注意力机制模块增强网络提取的特征;其次,针对多数叶片病害特征分布相对分散的问题,使用非对称混洗卷积模...

High Precision Identification of Apple Leaf Diseases Based on Asymmetric Shuffle Convolution

Aiming at the problems of low accuracy and poor generalization ability caused by the imbalance of samples between data sets, shooting angles, light changes and other actual imaging and environmental factors caused by apple leaf diseases, a type of asymmetric shuffle convolution neural network ASNet was proposed. Firstly, by adding an improved scSE attention mechanism module to the ResNeXt backbone network to enhance the network feature extraction; secondly, for the relatively scattered feature distribution of most leaf diseases, the asymmetric shuffle convolution module was used to replace the original residual module to expand the receptive field of the convolution kernel and the enhanced feature extraction ability, thereby improving the recognition accuracy and generalization ability of the model; finally, the use of channel squeeze and channel shuffling in the asymmetric shuffle convolution module made up for the grouping convolution. The defect of insufficient correlation between channels reduced the problem of low recognition accuracy of traditional network models caused by the imbalance between leaf diseases. Under the COCO data set evaluation index, the experimental results showed that compared with the Mask R-CNN whose backbone network was ResNeXt-50, the average test accuracy of this model reached 96.8%, which was increased by 5.2 percentage points, and the model size was reduced to 321 MB, a decrease of 170 MB. Tested by 240 field-collected and AI Challanger crop disease identification challenge apple leaf images, the test results showed that the average segmentation accuracy of the proposed model ASNet for apple black rot, rust, scab and healthy leaves reached 94.7%.