Smoke occlusion has an adverse effect on the target recognition of image-based homing guidance system. It is an urgent problem to improve the accuracy of smoke region segmentation recognition accuracy and reduce the false alarm rate. Some problems exist in Deeplabv3+basic algorithm such as missing and wrong segmentation, which result in serious loss of details and low overall segmentation accuracy. This paper proposes a smoke region segmentation algorithm based on improved Deeplabv3+model. An atrous spatial pyramid pooling (ASPP) structure of different-sensory field fusion based on the empty convolution is proposed to further expand the empty convolution receptive field and reduce the adverse impact of information loss. The backbone network is optimized and multi-scale fusion module is added to reduce the network parameters and calculation. The channel attention module is introduced to strengthen the feature learning ability of key channels, speed up the model training and improve the model segmentation accuracy. The experimental result shows that the improved Deeplabv3+model has an mean intersection over union (MIoU) ratio of 91. 03% in the test set and a segmentation efficiency of 12. 64 FPS, which has better result of segmentation than the traditional pattern recognition algorithm. The improved Deeplabv3+model achieves higher segmentation accuracy at the cost of less detection efficiency loss compared with the Deeplabv3+basic model, and the abilities of full scene understanding and detail processing are significantly improved. © 2021, Editorial Office of Systems Engineering and Electronics. All right reserved.
