## 6 Case Study
### Performance Study of the Optimal Model in the DIDN  
This experiment is designed to verify the effectiveness of the DBI model shown in Figure 3, where the implementation instance in the GALDAM is the optimal model of the DIDN (i.e., \(M^{sens}\)  
Figure 14: Visualized effects of different algorithms. Four bean images are used to illustrate the results of YOLOv3 [24; 37], HCADIS [36], and our proposed DL-DBIS.  
Figure 13: Augmented bean images: (**a**) original, (**b**) low density, (**c**) medium density, (**d**) high-density.  
discussed in Section 4.4. Figure 15 shows the performance study of the optimal model \(M^{gen*}\) of the DIDN to ten randomly selected bean images in term of DIP, DIR, and DIA, respectively.  
The DIP values stand for the ratio that classifying normal beans into defects, and the higher DIP values signify that a defect inspection scheme makes less mistakes. From the experimental results shown in Figure 15a, DL-DBIS has higher DIP values than HCADIS and YOLOv3, meaning that our proposed scheme is the best among the three. HCANDIS uses the HC transform to calibrate the prediction results from YOLOv3. Since most beans are of the circular shape, HCANDIS can successfully calibrate many mistakes of YOLOv3 and thus performs better than YOLOv3. Our proposed DL-DBIS can automatically generate new bean images in the training stage through the LBIG module and further prune the unnecessary bounding boxes by using HC transformation. Since low quality images are pruned by the model quality control in DL-DBIS, those augmented bean images can effectively increase the precision of the bean inspection model. Therefore, the DL-DBIS performs superior than HCADIS and YOLOv3.  
The DIR values stand for the ratio that the number of predictive defective beans to the number of actual defective ones, and the higher DIR values signify that a defect inspection scheme lose less defective beans. Comparing to the DIP, the DIR does not consider the cases that classifying normal beans into defects, which makes the DIR be the index to verify the bean quality after bean picking process by a specified scheme. From the experimental results shown in Figure 15b, DL-DBIS has much higher DIP values than HCADIS and YOLOv3, and most cases are over 60%, even approaching 80%. The results show that our proposed scheme has the best capacity to discover defective beans. This also means that if a client requests to remove all defective beans, our proposed scheme will be the best choice among the three to achieve this task. The key successful factor is that the DL-DBIS can generate effective new bean images, where other two cannot achieve. The high-quality augmented bean images not only shorten the training time, but also avoids the unconvergence problem in training the deep network, DIDN.  
The DIA values stand for the ratio that the area of predictive defective beans to the area of actual defective ones. That is, the DIA measures the overlapping degree of correctly predictive defective beans. The higher DIA values signify that a defect inspection scheme has higher probability to successfully remove defective beans. From the experimental results shown in Figure 15c, DL-DBIS has much higher DIA values than HCADIS and YOLOv3, and most cases are by 80%, whereas YOLOv3 only by 20%. Similar to previous studies, the success of the DL-DBIS comes from generating high-quality new bean images. The high DIA implies that the arm head has less probability of sideswiping beans nearby, which might muddle next rounds of bean picking.