Real-time segmentation of weeds in cornfields based on depthwise separable convolution residual network

Traditional artificial spraying of pesticides leads not only to lower utilisation of pesticides but also to environmental pollution. However, intelligent weeding devices can identify weeds and crops through sensing devices for selective spraying, which will effectively reduce the use of pesticides. The accurate and efficient identification method of crops and weeds is very crucial to the development of the mechanised weeding model. To improve the segmentation exactitude and real-time performance of crops and weeds, we propose a lightweight network based on the encoder-decoder architecture, namely, SResNet. The shuffle-split-separable-residual block was employed to compress the model and increase the number of network layers at the same time, thereby extracting more abundant pixel category information. Besides, the model was optimised by a weighted cross-entropy loss function due to the imbalance of pixel ratios of background, crops and weeds. The results of the experiment prove that the method presented can greatly improve the segmentation accuracy and real-time segmentation speed on the crops and weeds dataset.