## 3 Results and Discussion
### Performance Evaluation of Defect Detection
Params refers to the total number of parameters that need to be trained in the network model, while FLOPs refers to the number of floating-point operations per second. The smaller the values of the two, the more streamlined the model. Figure 10 presents representative examples of detection results for different defect types in this test, while Table 2 summarizes the key performance metrics of various models on the simulated dataset. In the simulated thermal image shown in Figure 10a, three types of defects--missing solder ball, missing solder ball and pad, and bridge--are accurately detected by the model, with corresponding confidence scores of 0.91, 0.91, and  
0.94, respectively. In the experimental infrared image shown in Figure 10b, the detected defects include a missing solder ball and a bridge, with confidence scores of 0.83 and 0.85. From the perspective of detection accuracy, YOLOv8 demonstrates the best overall performance, achieving an mAP50-95 of 94.5%, slightly surpassing other models. This indicates greater robustness across varying IoU thresholds. All models exhibit excellent performance in terms of mAP50, with values exceeding 99.3%. Among them, YOLOv8, YOLOv9t, and YOLO11n each reach 99.5%, confirming the reliability of the YOLO series in high-confidence detection tasks. YOLOv10n shows a slightly lower mAP50, which may be attributed to reduced feature representation capability due to its lightweight architecture. In terms of inference speed, both YOLOv8 and YOLOv10n achieve 667 FPS, significantly outperforming YOLO11n and YOLOv9t. Notably, YOLOv10n maintains the same inference speed as YOLOv8 while reducing the number of parameters by 10%, showcasing the advantages of a lightweight design. YOLOv9t has the lowest FPS among the models. Although it delivers high accuracy, the trade-off in speed makes it more suitable for high-precision but non-real-time applications. Regarding model complexity, YOLOv9t has the smallest number of parameters, yet its FLOPs are comparable to those of YOLOv10n, suggesting a compact structure but suboptimal computational efficiency. In contrast, YOLO11n exhibits the lowest FLOPs--22% lower than those of YOLOv8--and a parameter count similar to YOLOv10n, reflecting an efficient architectural design for computational performance. Although YOLOv8 does \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **mAP50** & **mAP50-95** & **FPS** & **Params (M)** & **FLOPs (G)** \\ \hline YOLOv8 [27] & 99.5\% & 94.5\% & 667 & 3.01 & 8.2 \\ YOLOv9t [28] & 99.5\% & 93.5\% & 387 & 2.01 & 7.9 \\ YOLOv10n [29] & 99.3\% & 92.9\% & 667 & 2.71 & 8.4 \\ YOLO11n [26] & 99.5\% & 93.0\% & 556 & 2.59 & 6.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of different YOLO-based models on the simulation dataset, including detection accuracy (mAP at IoU thresholds of 0.5 and 0.5–0.95), inference speed (frames per second, FPS), model size (number of parameters, params), and computational cost (floating-point operations, FLOPs). Figure 10: Example detection results using the YOLO11 model: (**a**) defect identification in simulated thermal images; (**b**) defect localization in experimentally captured infrared images, demonstrating the model’s applicability to both data sources. not achieve the lowest parameter count or FLOPs, it provides the best trade-off between accuracy and speed. For YOLO11n, the mAP50 is on par with YOLOv8 and YOLOv9t, and it surpasses YOLOv10n. Its FLOPs are the lowest among the compared models, ranging from 18% to 24% lower, which makes it well-suited for low-power environments. It also achieves a high frame rate of 556 FPS, demonstrating strong inference speed. The lightweight design also facilitates deployment on edge devices. Overall, YOLO11n presents a viable solution for intelligent industrial defect detection by offering high accuracy, computational efficiency, and strong real-time performance.