A simultaneous particle size and position prediction model based on Faster-RCNN and VGG16 convolutional neural networks (CNNs) is constructed for the defocused images of particles obtained by a dual-camera imaging system. Nine different dots with diameters ranging from 50 to 350 μm are taken in a depth range of 75 to 95 mm (about 9 to 10 times the depth of field of the imaging system) for the training of the proposed model, and the proposed model is compared with the processing method based on the depth from defocus (DFD) model. The measurement results show that compared with the processing method based on the DFD model, the particle depth measurement range of the CNN model is improved, the diameter measurement error is reduced, and the depth measurement error is increased. The standard particles with a particle size of 120 μm flowing in a circulating sample cell are further photographed by a dual-camera system, and the images are processed by applying the proposed CNN model. The relative error of the particle size prediction results ranges from -8% to 8%.