At the same time, it is difficult to measure the volume of ore and rock in sheltered transportation vehicles. The level meter detection method requires the use of multiple level meters and control instruments, which requires a fast response time. At the same time, it is greatly affected by severe weather such as rain and snow, dust and fine slag in the mine, which makes daily maintenance inconvenient. Sensor equipment has a high cost and a high failure rate. In addition to the traditional detection methods of manual counting and weighbridge weighing, there are sensor detection methods and material level meter detection methods. It is also of great significance to accurately grasp and control the output of each part of ore and rock for the realization of efficient utilization of mineral resources and effective production management. Transportation of ore and rock will have a direct impact on the completion of mine production tasks and the performance appraisal of truck drivers. This not only seriously distorts the production data of open pit mines, affecting the accurate distribution of ore, but also causes a waste of labor, vehicles and oil. Since there is no online measuring device for the truck loading equipment currently used in the open-pit mines, the output of each part of ore and rock is roughly measured by multiplying the number of carrying vehicles by an agreed single truck loading capacity. Loading capacity measurement is a daily production management task in mines. The experimental results show that the model has good generality and can be applied well to the actual production of open-pit mines. In addition, this paper uses 400 real mining truck images of open-pit mines to verify the model and the average absolute error is 2.53 m 3. The average absolute error is 17.85 cm 3. The experimental results show that the model has high prediction accuracy. Root mean square error (RMSE) and mean absolute error (MAE) are used to evaluate the fitting effect of the model. By using the labeled image data of five kinds of mining truck loading volume, the arbitrary loading volume detection of mining trucks is realized, which effectively solves the problem of a lack of labeled data types caused by the difficulty in obtaining mine data. Then, the loading volume of mining trucks is calculated by using the classification results and the least squares algorithm. ![]() The classification results are displayed and the possibility of each category is determined. After image preprocessing, the VGG16 network model is used to pre classify the ore images. ![]() The training and test data of the model consists of 6000 sets of images taken in a laboratory environment. Aiming at the addressing the current problems of low accuracy and high cost of the detection of the loading volume of mining trucks, this paper proposes a mining truck loading volume detection model based on deep learning and image recognition. Detection of the loading volume of mining trucks is an important task in open pit mining.
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