# Buffer-YOLOv8: An Enhanced Cascaded Framework for Concealed Object Detection
## 6 Simulation Results and Discussion
### Qualitative Analysis  
To further validate the efficiency of the proposed FER-YOLOv8 algorithm and proposed research framework on a PMMW imaging dataset, a qualitative analysis was performed to prove the supremacy of the proposed algorithm. For a deep qualitative comparison between the proposed models and the other models described above, consider the true positive criteria in which the positive sample is selected randomly from the ground truth and all the above-mentioned trained models detect the person and gun class accurately, as shown in Figure 20.  
The certainty of detecting the gun in a particular positive sample using the SOTA tiny YOLOv3 algorithm is 0.65. The certainty of detecting the gun in a particular positive sample using the YOLOv8m-WF algorithm is 0.95. However, the prediction certainty of the gun using the proposed FER-YOLOv8 algorithm is 0.97, which is higher than the SOTA tiny YOLOv3 algorithm and other YOLOv8 variants. The connected regions in the detection of the person class in YOLOv8n-WF are less than for the other models. However, the prediction score in the YOLOv8n-WF model for the person class and the gun class is relatively lower than the YOLOv8 variants, which makes the YOLOv8n-WF model difficult to use. According to the above-presented analysis, when the criteria are based on the detection scores of the person and gun classes, the recommended FER-YOLOv8 model performs better than the other models. Figure 19: Comparison of computational complexity of various YOLO models. To ensure consistency and robustness in the accurate detection of concealed weapons with higher certainty, a comparison of the distribution of the confidence scores in detecting the gun and person classes for the above-mentioned trained models was compiled and is illustrated in Figure 21. The white thin line in each violin plot represents the median of the confidence scores. The median value of the confidence scores of the SOTA tiny YOLOv3 algorithm is 0.80 for the gun class. The minimum and maximum confidence scores of the tiny YOLOv3 algorithm observed on the test dataset are 0.27 and 0.89, respectively. The median values of the confidence scores of the YOLOv8n, YOLOv8n-WF, YOLOv8m, and YOLOv8m-WF models are 0.88, 0.91, 0.94, and 0.94, respectively. The higher median values of the YOLOv8 variants divulge that the reliability in detecting a gun accurately in each test image inclines towards a higher quantile region as compared to the tiny YOLOv3 algorithm. The above-discussed performance can also be visualized by the distribution shape of the confidence scores, as represented in Figure 21. The widths of the upper lobes of the violin plots for the YOLOv8m and YOLOv8m-WF models are wider than the widths of the upper lobes of the tiny YOLOv3 and YOLOv8n models, which indicates that the density of the confidence score values for the YOLOv8m and YOLOv8m-WF models is higher in the third quantile region. Hence, the YOLOv8 variants experience more robustness and consistency in the detection of concealed objects than the tiny YOLOv3 algorithm. Similarly the minimum and maximum confidence scores of the YOLOv8m-WF algorithm are 0.27 and 0.97. The median value of the confidence scores of the proposed FER-YOLOv8 algorithm is 0.96 for the gun class, which is higher than the above-discussed models. The minimum and maximum confidence scores of the tiny YOLOv3 algorithm observed on the test dataset are 0.27 and 0.89. Therefore, the extensive experiments show that the proposed FER-YOLOv8 algorithm is more reliable for CWD systems. Figure 20: Qualitative analysis of the trained models on an accurately detected positive sample. (**a**) Positive sample from ground truth. (**b**) Tiny YOLOv3 prediction. (**c**) YOLOv8n prediction. (**d**) YOLOv8n-WF prediction. (**e**) YOLOv8m prediction. (**f**) YOLOv8m-WF prediction. (**g**) Proposed FER-YOLOv8 prediction. The quality of the trained models can be improved by reducing the false positive rate in the detection of concealed weapons such as small guns, etc.