Abstract: The strong randomness and irresistibility of the transmission lines covered with ice and snow leads to the extreme difficulty in handling such emergency, therefore the intelligent edge recognition ability of icing monitoring terminal is urgently needed. This paper proposes a front-end identification method of transmission line icing grade based on lightweight receptive field feature expression network. Firstly, a lightweight convolutional neural network named MobileNetV3 is utilized to extract feature information of the icing image, and the receptive field block is introduced to enlarge the mapping area of the model to the icing image, thereby enhancing the feature extraction ability of the network. Then the multi-scale target detection network SSD (single shot multibox detector) is used to achieve the thickness identification and monitoring of icing image. Finally, the ice image perceived in the real scene is experimented in the edge intelligent device with limited computing resources. The experiment results show that the proposed edge intelligent monitoring method can achieve the front-end recognition of ice grade and can maintain high recognition accuracy for ice images collected in the extreme weather. This method greatly avoids long-distance transmission of the ice images and achieve the edge intelligent autonomy of ice cover monitoring in the extreme weather, which has strong generalization ability and practical application value.