A lightweight convolutional neural network based on dense connection for open-pit coal mine service identification using the edge-cloud architecture

Abstract

AbstractRemote sensing is an important technical tool for rapid detection of illegal mining behavior. Due to the complex features of open-pit coal mines, there are few studies about automatic extraction of open-pit coal mines. Based on Convolutional Neural Network and Dense Block, we propose a lightweight densely connected network-AD-Net for the extraction of open-pit coal mining areas from Sentinel-2 remote sensing images, and construct three sample libraries of open-pit coal mining areas in north-central Xinzhou City, Shanxi Province. The AD-Net model consists of two convolutional layers, two pooling layers, a channel attention module, and a Dense Block. The two convolutional layers greatly reduce the complexity of the model, and the Dense Block enhances the feature propagation while reducing the parameter computation. The application is designed in different modules that runs independently on different machines and communicate with each other. Furthermore, we create and build a unique remote sensing image service system that connects a remote datacentre and its associated edge networks, employing the edge-cloud architecture. While the datacentre acts as the cloud platform and is in charge of storing and processing the original remote sensing images, the edge network is largely utilised for caching, predicting, and disseminating the processed images. First, we find out the optimal optimizer and the optimal size of the input image by extensive experiments, and then we compare the extraction effect of AD-Net with AlexNet, VGG-16, GoogLeNet, Xception, ResNet50, and DenseNet121 models in the study area. The experimental results show that the combination of NIR, red, green, and blue band synthesis is more suitable for the extraction of the open-pit coal mine, and the OA and Kappa of AD-Net reach 0.959 and 0.918 respectively, which is better than other models and well balances the classification accuracy and running speed. With this design of edge-cloud, the proposed system not only evenly distributes the strain of processing activities across the edges but also achieves data efficiency among them, reducing the cost of data transmission and improving the latency.