Coal dust image segmentation method based on improved DeepLabV3+

When the traditional semantic segmentation network is used to segment the small coal dust particles, there are some problems such as easy loss of deep information and unclear detail extraction. In order to solve this problem, a coal dust image segmentation method based on improved DeepLabV3+ is proposed. DeepLabV3+ network model is improved in three aspects. ① In the encoder, the CA-MobileNetV3 lightweight module is used to replace Xception to achieve characteristic extraction and ensure more detailed and accurate characteristic extraction. ② The atrous rate is improved in the atrous spatial pyramid pooling(ASPP) module to make it more suitable for extracting small coal dust particles. ③ A global attention up-sample(GAU) module is introduced into the decoder to weight the low-level characteristic information when the calculation amount is small. And the high-level characteristic information is used to guide the low-level characteristic information to realize characteristic fusion. The GAU module uses a global up-sampling mechanism to replace the up-sampling mechanism of the decoder. The characteristic information of the coal dust particles is not attenuated after long-distance transmission. And the method is more conducive to capture the edge detail information of the coal dust particles. The experimental results show that the recall rate of the improved DeepLabV3+ network model on the coal dust data set is 90.26%, and the accuracy is 89.23%. Compared with other network models, the improved DeepLabV3+ network model can effectively enhance the learning ability of coal dust characteristics, obtain more detailed information, greatly shorten the training time, and has better segmentation effect on small targets.