Deep Learning-Based Automatic Monitoring Method for Grain Quantity Change in Warehouse Using Semantic Segmentation

Abstract

The quantity security of stored grain in warehouses is crucial for a country. Since manual inspection of stored grain quantity is inefficient, some online automeasurement methods have been proposed recently, but they are either time-consuming or too expensive to be popularized. This article proposes a deep learning-based method to automatically monitor changes in grain quantity of granaries. First, the image of the same scene in a granary is taken at different moments using a device that integrates a camera and an infrared laser rangefinder. Then, a deep semantic segmentation model based on an encoder-decoder framework is developed to extract the grain loading line and grain surface of the image. Finally, the distance and area between the extracted grain loading line and grain surface are calculated and compared with the previous measured one to determine whether the grain quantity of the granary has changed. To improve the accuracy of the segmentation results, a novel reverse attention model is proposed to provide guidance information to fuse low-level features, which calculates multiscale attention maps based only on the output of the last layer of the encoder. Furthermore, the proposed method and infrared laser rangefinder-based method are combined to get more accurate grain volume after an abnormal change of grain quantity is detected by our method. Experimental results show that our method is effective and feasible for monitoring changes in grain quantity and outperforms the state-of-the-art methods on semantic segmentation.