The invention discloses a large volume parenteral foreign body detection method, a system, a medium and a device based on deep learning and target tracking, and the method comprises the steps: carrying out the image preprocessing of a plurality of collected continuous images, combining a target detection algorithm and a target tracking algorithm, and achieving the fusion of target detection and target tracking and the precise positioning and tracking of foreign bodies; firstly, preproce3ssing a sequence image, then using a Faster RCNN neural network for carrying out target detection on a firstframe of image to obtain the initial position of each suspected target, then tracking the positions of each target in later frames through a CSRDCF target tracking algorithm to obtain the motion trail of each suspected target, and obtaining the motion trail of each suspected target, and finally, according to the track characteristics, performing classification by using an adaptive classificationalgorithm based on a semi-naive Bayes principle, and eliminating noise interference. Experiments show that the method not only can greatly improve the detection speed, but also can greatly improve thedetection precision, and meets the requirements of industrial production precision and real-time performance.