# Deep Learning-Based Oyster Packaging System
## 3 Experiments and Results  
After designing and selecting each module, the communication between each module was established and integrated into a complete oyster packaging system based on deep learning, and the physical diagram of the system after the hardware connection of each module was completed as shown in Figure 11. The oyster automated packaging system constructs an oyster detection model based on the deep learning target detection algorithm and the lightweight technology of pruning and composes an oyster intelligent perception model with the oyster tracking and counting model to realize the intelligent perception and counting tasks for oysters on the production line. Dynamic grasping tests were conducted on a single oyster on the automated production line. After the pre-planning was completed, the oyster grasping tests were conducted on the actual production line, and the whole grasping test process is shown in Figure 12. Oysters on the production line were detected with the deep-learning oyster detection model, and then, the oysters were quickly tracked and localized by the tracking and counting model. The test results showed that the model can accurately recognize and track  
Figure 10: Loss function variation image for oyster detection model. oysters when they are moving, and Kalman filtering can accurately predict oyster location information. The control cabinet guided the mechanical arm to reach the predicted grasping position and achieved accurate grasping and smooth transferring of oysters, which completed the oyster packaging task. The accuracy of oyster pose estimation was tested with the system. Oysters were randomly placed on the conveyor belt, ensuring that the camera was fixed and the relative position to the mechanical arm remained unchanged. The coordinates of the oysters relative to the mechanical arm, the coordinates of the oysters detected by the recognition and localization algorithm, and the transformed coordinates by the mapping matrix were recorded. The results of the six sets of tests are shown in Table 2. The maximum opening and closing distance of the mechanical claw used in this study is 120 mm, and the absolute error of the oyster position estimation is not more than 7 mm, which is within the permissible error range of the mechanical claw. Figure 11: Components of the oyster packaging system. Figure 12: Flowchart of the oyster grasping process. The system was tested for oyster dynamic and static grasping. Six sets of static grasp tests were conducted for oysters, with six oysters randomly placed in each set. The observations were made whether the oyster would be adjusted to a better grasping position after passing through the baffle plate for position adjustment and whether the conveyor belt stopped running when the oyster passed through the infrared sensor. When an oyster reached the infrared sensor sensing range, the conveyor stopped running, the mechanical claw grasped the oyster, which triggered the infrared sensor, and then the conveyor continued to run. The number of successful oyster grasps and the time taken for the six groups of oyster grasps were recorded. The results of the experiments are shown in Table 3. In the six rounds of grasping experiments conducted, there were only two grasping failures, and in the two grasping failures, it was the oyster that slipped during grasping. Comprehensively analyzing the results of the six rounds of grasping experiments, the average success rate of grasping was 94.44%. The static grasping experiments were conducted on oysters in six rounds, for a total of 36 grasps, and the total time taken for grasping in each round was recorded. The average time taken for static grasping and packing of a single oyster was calculated to be about 7.8 s.  
For the oyster dynamic grasping test, an oyster was placed on the top of the conveyor belt, and the time delay generated by the mechanical arm movement for grasping was recorded. Twenty-five grasping tests were performed at different belt speeds, and the number of successful grasps was recorded.