Image Segmentation Method for Sweetgum Leaf Spots Based on an Improved DeeplabV3+ Network

Abstract

This paper discusses a sweetgum leaf-spot image segmentation method based on an improved DeeplabV3+ network to address the low accuracy in plant leaf spot segmentation, problems with the recognition model, insufficient datasets, and slow training speeds. We replaced the backbone feature extraction network of the model’s encoder with the MobileNetV2 network, which greatly reduced the amount of calculation being performed in the model and improved its calculation speed. Then, the attention mechanism module was introduced into the backbone feature extraction network and the decoder, which further optimized the model’s edge recognition effect and improved the model’s segmentation accuracy. Given the category imbalance in the sweetgum leaf spot dataset (SLSD), a weighted loss function was introduced and assigned to two different types of weights, for spots and the background, respectively, to improve the segmentation of disease spot regions in the model. Finally, we graded the degree of the lesions. The experimental results show that the PA, mRecall, and mIou algorithms of the improved model were 94.5%, 85.4%, and 81.3%, respectively, which are superior to the traditional DeeplabV3+, Unet, Segnet models and other commonly used plant disease semantic segmentation methods. The model shows excellent performance for different degrees of speckle segmentation, demonstrating that this method can effectively improve the model’s segmentation performance for sweetgum leaf spots.