## 5 Experimental Results and Discussions  
To evaluate the performance of the proposed method, the VISFFB was configured, as shown in Figure 2. For acquiring high-resolution images of the flexible fluid bags, two MX-A201R-26 CMOS sensor cameras with a resolution of 3627 \(\times\) 5488, each equipped with a V3528-MPY lens, were positioned at each inspection location. These cameras capture 70 \(\times\) 100 mm\({}^{2}\) areas of the upper and lower parts of the bags. The cameras are connected to a single PC, where detection of moving objects and differentiation between foreign particles and air bubbles are performed using deep learning-based object detection algorithm, Faster RCNN. The PC is equipped with an Intel Core i9-10900 KF @3.70 GHz CPU, 64 GB RAM, and an nVidia RTX-3090 GPU, running on Windows 10 OS. High-resolution sequence images are acquired from both cameras through a frame grabber, allowing the foreign particle detection algorithms to run simultaneously and in real-time for the sequence images from both cameras. The test bags were 500 mL flexible non-PVC transparent bags filled with 5% dextrose solution. The dataset included 35 defective fluid bags containing a few foreign particles of 150-200 um in size and 100 normal fluid bags with no foreign particles. The fluid bags were loaded in sets of five for inspection, with each fluid bag being examined individually. The main rotor's two inspection positions allow for parallel examination of these sets of fluid bags. Figure 9 illustrates the network setup and communication structure for aggregating inspection results and controlling the main rotor. Results from each PC are sent to the main control PC, which compiles the inspection results to identify defective fluid bags. The control PC then manages the discharge paths for normal and defective fluid bags and rotates the main rotor to direct each fluid bag to the correct discharge conveyor or defective product container. As shown in Figure 10, the system includes two cameras at each of the five inspection positions, with lights being positioned opposite the cameras and beneath the fluid bags. This setup ensures a clear distinction between foreign particles and air bubbles by minimizing surface reflections and enhancing the visibility of internal objects. The rear lighting reduces surface reflections, while the lower lighting highlights air bubbles. This causes the air bubbles to refract light and appear bright in the camera images, whereas foreign particles do not refract light, making them more distinguishable. Figure 9: Network and communication structure for information and control. In Figure 11, image (a) shows a fluid bag captured with only rear lighting, while image (b) shows the same fluid bag with both rear and lower lighting. Comparing these images reveals that lower lighting makes the air bubbles more visible by refracting the lower light. Image (c) further demonstrates this effect, highlighting small air bubbles even within dark printed areas, showing that lower lighting effectively distinguishes air bubbles from foreign particles. Foreign particles do not refract or transmit the lower light, resulting in minimal change between images with and without lower lighting, thereby improving the distinction between foreign particles and air bubbles. To extract moving objects, binary differencing or motion-compensated differencing methods are commonly used for bottles or vials. However, for flexible fluid bags, these  
Figure 11: Comparison of the effect of lower light for a flexible fluid bag. Lower light improves the distinguishability between bubbles and particles. (**a**) Image captured using only backlight; (**b**) image captured using both backlight and lower light. (**c**) Comparison of the bubble and particle regions without lower light and with lower light: The left images are copied from (**a**), and the right images are copied from (**b**) at the same positions. The red circles indicate bubbles, and the blue rectangle indicates a particle.