An unstructured road recognition method based on the fusion of improved DeepLabv3+and SE attention mechanism
Aiming at the problems of low extraction accuracy and inability to effectively extract unstructured road information when extracting unstructured road such as open-pit mine,an open-pit road recognition method based on improved DeepLabv3+network fusion SE attention mechanism was proposed,and cavity convolution parallel sampling with different sampling rates was used to obtain advanced features of target images.SE attention module was introduced to carry out feature balance between high-level features obtained from sampling and low-level features extracted from backbone network,so as to distinguish the importance of different features and improve the accuracy of feature information after fusion.The experimental results show that the network is superior to other algorithms in mine road recognition,and all the evaluation indexes of road recognition are improved,which can effectively identify the unstructured open-pit mine road.