Leaf image segmentation based on DeepLabV3+ and channel attention mechanism

Image segmentation holds significant research significance and practical applications in the fields of computer vision and image processing, particularly in agricultural production, environmental monitoring, and digital botany. However, there is relatively limited research on its application to paper mulberry, prompting us to construct our own semantic segmentation dataset for paper mulberry. This dataset comprises 2576 images, which were meticulously annotated using the eiseg tool. Data augmentation techniques were also employed during the training process. Regarding the model, this study is built upon the DeepLabV3+ architecture, with the introduction of an attention mechanism known as the Squeeze-and-Excitation Block (SE-Block). Furthermore, modifications to the dilation rates of atrous convolutions within the ASPP module were made to achieve a larger receptive field. Experimental results demonstrate a notable improvement in segmentation accuracy, with a gain of 0.8% over the original algorithm. This enhancement is particularly beneficial for precise leaf area calculations.