When the silage harvester operates in the field, the filling and throwing system has a great influence on the efficiency of the silage harvester. Aiming at the feeding process of silage harvester, this study designs a precise throwing system for silage machine based on rotating target detection in order to realize the adaptive throwing of silage crumbs to the wagon compartment. In this study, a filling rule is proposed through the definition of ideal drop point and actual drop point. At the same time, an improved YOLOv5s (Rotation-YOLOv5, R-YOLOv5) was proposed to further realize the accurate detection of rotating targets using machine vision. The feedback of the target and the actual falling point were then calculated to control the movement of the throwing cylinder. The self-adaptive control was achieved in the throwing cylinder using R-YOLOv5. The network structure of the baseline was also optimized to predict the rotating carriages. A new prediction channel of rotation angle was then added to the Head part of the original YOLOv5. The virtual environment was built using Python3.9. Pytorch1.6 was chosen to train the R-YOLOv5 rotating target detection model on the Pytorch deep learning framework. Once the number of training rounds reached 60, the mAP values were all stable at 0.97, indicating a stable system. The improved R-YOLOv5 was recognized as a rectangle with a rotation angle. The recognition range was synchronized with the angle of the carriages. The training data was highly compatible with the carriages for better recognition. In addition, the throwing control was set under the distance between the actual and target drop position. A rotating carriage identification and a field test were carried out to develop the control flow. The test results show that the improved R-YOLOv5 was better performed on the rotating compartments at different rotating angles with an average of more than 97% before, indicating better detection; When the projectile barrel was rotated in the range of -60°-60°, angular velocity was less than 15°/s. The average accuracy of R-YOLOv5 target detection was higher than 90%; better recognition was achieved in the high accuracy of the actual drop position output, where more material was outflowed in unit time. The material outflow of the throwing cylinder in unit time was mainly related to the cutter's rotational and harvesting walking speed in the silage harvester. The correct rate of 85% or more was obtained to fully meet the harvesting requirements at the cutter rotational speed of 1 200 r/min, forward speed of 6 km/h, and the amount of material out of the unit between the Ⅳ level or more; The visual information was inputted by Arduino controller along with R-YOLOv5. The motion of the throwing cylinder was realized with 0.533 cm/pt, as the ratio of the actual distance to the image pixels. The average error between the theoretical and actual motion trajectory of the throwing cylinder was kept within 4%, which fully met the design requirements. The finding can provide a strong reference to develop the adaptive throwing-filling system in the self-propelled silage harvester.