An improved DeepLabV3+ network-based deep learning segmentation method for thermal image water-shorelines

Abstract

The water-shorelines segmentation of thermal image is essential to the visual perception technologies and applications of unmanned surface craft. However, the traditional semantic segmentation algorithms have the problems of limited accuracy and low efficiency, which significantly restricts the segmentation performance. Although the segmentation accuracy of convolutional neural network (CNN) is greatly improved compared with these segmentation algorithms, the effect of same model for different regions is obviously different due to the uneven distribution of water-shoreline scene categories in different regions. Therefore, this study proposes an improved DeepLabV3+ network-based segmentation method for the water-shorelines by adding a SE channel attention mechanism and replacing its original backbone network. To validate the performance of the proposed method, an appropriate data set and several assessment indexes were also established. The experiments compared with several conventional algorithms shown that the obstacle interaction degree and mIoU of the proposed method can highly reach to 72.03 % and 90.17 %, which improved 4.81 % and 1.55 % compared with the DeepLabV3+ network model. Even for the limited sample images, it can also more accurate segmentation for small obstacles, and clearer extract for the water-shoreline feature information.