Steel bar quantity detection method and system based on deep neural network

The invention discloses a steel bar quantity detection method and system based on a deep neural network. The method comprises the steps: generating a pre-training model according to a third-party object detection data set and based on an improved Faster RCNN algorithm; inputting a steel bar data set including labeling information, performing first data enhancement conversion on pictures in the steel bar data set, and performing transfer learning training on the pre-training model to generate a steel bar detection model; acquiring a to-be-detected picture including the cross section of the steel bar, obtaining a first detection frame through the steel bar detection model, performing second data enhancement conversion on the to-be-detected picture to obtain a plurality of second enhanced pictures, and obtaining a second detection frame through the steel bar detection model; matching the first detection frame and the second detection frame according to the coordinate information, and obtaining the detection number of the steel bars based on the confidence degree of the steel bars and the overlapping degree of the detection frames. The steel bar detection and warehousing speed is increased, manpower and material resources are saved, and the detection accuracy is improved.