Display omitted •The conveyor belt and high-speed camera were used to collect the DTD of particles.•The MFR prediction model was established by fitting the mass with image by ANN.•Calculation of MFI by CNN combined with error correction.•The test results for glass beads and quartz sand were within the tolerance range. A machine vision and neural network-based method for the quantitative detection of hopper discharge characteristics based on the discharge time distribution is proposed. Glass beads and quartz sand were utilized as test objects. The prediction model of the relationship between the particle mass and the pixel value of the image was established by an artificial neural network, and the mass flow rate (MFR) was calculated via image prediction. The average relative errors of the predicted MFR for glass beads and quartz sand were found to be −1.31 % and −2.02 %, respectively. Based on the particle marking method, a convolutional neural network was used to classify the image according to whether there were marked particles in the image, and the mass flow index (MFI) was calculated after error correction. The average relative errors of the predicted MFI values for glass beads and quartz sand were found to be −1.43 % and 0.82 %, respectively.