## 4 Results and Discussion
### YOLOv11n-seg Model Selection and Comparison with Other Models
#### 4.4.1 YOLOv11n-seg Model Architecture  
The YOLOv11n-seg model used in this project contains 265 layers and approximately 2.8 million parameters. It has a computational power of 10.2 GFLOPs and was trained using PyTorch version 2.1.1 with CUDA 12.1. The average inference time for this model is  
Figure 20: The original (**left**) and segmented (**right**) detection of mixed sharp and smooth objects using an RGB camera. reported to be 6.1 milliseconds, with approximately 0.7 ms for preprocessing and 2.1 ms for postprocessing, keeping the total image processing time under 9 milliseconds--thus making it highly suitable for real-time industrial systems. As shown in Figure 21, the sharp-edged fragments were visually distinguishable by their angular outlines and reflective surfaces, while smoother objects appeared more rounded and compact. This image served as the ground truth reference for evaluating segmentation model performance. Figure 22 shows the output of the YOLOv8n-seg model on a test image of recycled materials moving on a conveyor belt. In this image, the model attempts to classify the objects into two main categories: the sharp class (with angular or pointed edges), highlighted in blue masks; and the smooth class (with rounded or soft edges), highlighted in cyan masks. The model assigns a confidence score for each detected object, indicating how certain it is about the classification. At the bottom part of the image, two large aluminum fragments with clearly defined sharp edges had been correctly classified as sharp, with high confidence scores of 0.92 and 0.93, respectively. In contrast, several smaller, more rounded stone-like objects had been accurately labeled as smooth, with confidence scores ranging between 0.80 and 0.84. However, the image also illustrates two borderline cases where the model had difficulty classifying. One object in the center of the image, which visually appeared to have a smooth shape and curved edges, had been misclassified as sharp with a low confidence score of 0.388. Another object near the top-left corner was also labeled as sharp but with a low confidence score of 0.48, suggesting the model's uncertainty in its decision. This image plays a critical role in evaluating the performance of YOLOv8n-seg. While the model performs well in most cases, its lower accuracy in borderline examples--especially those with confidence scores below 0.5--indicates reduced reliability under complex edge patterns or when visual distinctions are diminished by lighting and shadow variations. This highlights the importance of incorporating additional post-processing steps or adopting more advanced models, such as YOLOv11, for industrial scenarios that demand high classification precision. Figure 21: Original test image showing a mixture of metallic and plastic particles with varying geometries. The model output in Figure 23 demonstrates that all of the materials in the image were correctly classified into two categories, i.e., sharp and smooth, and the confidence scores for most predictions were also above 0.9 (e.g., sharp objects were identified with 0.92 and 0.93 confidence, while smooth objects scored 0.83, 0.80, and 0.84). No misclassifications were observed, and all of the objects were successfully detected. Figure 23: Model output from YOLOv11n-seg when applied to the test image. All visible particles were correctly segmented and classified into sharp (blue masks) and smooth (cyan masks) categories, with high confidence scores. The results demonstrate the model’s precise shape-based discrimination, which is essential for real-time separation and protection tasks in BECS systems. Figure 22: The YOLOv8n-seg model output on the test image. The model identified sharp and smooth objects with varying confidence scores. One misclassification was observed—an object with smooth edges was labeled as sharp with a low confidence of 0.388. In terms of evaluation metrics, the following results were obtained:  
* mAP@50 : 0.824 for sharp, and 0.820 for smooth. * mAP@50-95: 0.646 for sharp, and 0.655 for smooth.