# Enhanced YOLOv8 with BiFPN-SimAM for Precise Defect Detection in Miniature Capacitors
## 4 Experiments and Analysis of Results
### Comparison with Other Algorithms  
According to the empirical findings displayed in Table 5, the suggested small-scale capacitor identification model has significant benefits in comparison with the current cutting-edge target detection algorithms, such as SSD, Faster RCNN, and YOLO versions 5n, 7-tiny, and 8. The model introduced in this paper, referred to as \"Ours\", achieved a noteworthy enhancement in mean average precision (mAP) at a threshold of 0.5--recording \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Parameters/M** & **FPS/f.s-1** & **[EMAIL_ADDRESS]** \\ \hline YOLOv8n & 3.01 & 96 & 86.3 \\ YOLOv8n + Simam & 3.01 & 94 & 90.3 \\ YOLOv8n + BiFPN & 3.37 & 83 & 90.0 \\ YOLOv8n + WIoU & 3.01 & 99 & 88.6 \\ YOLOv8n + Simam + WIoU & 3.01 & 93 & 93.0 \\ YOLOv8n + Simam + BiFPN + WIoU & 3.37 & 78 & 95.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of results from ablation experiments. a significant 95.8% as opposed to the original YOLOv8's 86.3%. This improvement marks a 9.5% increase in mAP, which is substantial considering the operational parameters of the model. Our model not only excelled in precision but also demonstrated an impressive frame rate of 78 FPS, which, while slightly lower than that of YOLOv7-tiny, is considerably higher than that of the more computationally demanding models like Faster RCNN. This balance of speed and accuracy indicates that our model is not only more precise but also operationally efficient. The marginal increase in the number of parameters from YOLOv8 to our model (from 3.01 M to 3.37 M) is a modest trade-off for the significant gains in detection accuracy. In comparison with other models' recall rates, our model demonstrated an approximate 2.5% improvement over the highest-performing model in the category, Gold-YOLO, which has a recall rate of 87.9%. This enhancement signifies that our model is more proficient in correctly identifying a greater number of true positives, i.e., defects. This capability is particularly crucial in the context of micro-capacitor defect detection, as it ensures the comprehensive identification of all potential defects. The slight increment in model complexity did not impede the processing speed, as evidenced by the fps metric, which is crucial for real-time detection tasks. Other updated models' performance on our MCSD dataset demonstrated a minor reduction from their findings on their respective originally selected public datasets, aside from the foundational models in the YOLO series. This discrepancy in performance might be explained by these models' restricted capacity for generalization, especially in the narrow field of micro-capacitor defect detection. These restrictions draw attention to the difficulty of modifying current models to fit extremely precise and subtle jobs, highlighting the necessity for customized methods in machine vision applications meant for complex micro-capacitor defect detection. These discoveries hold manifold implications. Primarily, the substantial increase in mAP signifies a significant augmentation in the model's capacity to identify minute capacitive anomalies--a pivotal facet in ensuring quality control within the realm of electronics manufacturing. Second, the preservation of computational efficiency, i.e., the steadfastness in computation and fps, underscores the model's potential for real-time applications. Sustaining this equilibrium between high detection accuracy and operational efficiency stands as a linchpin for practical implementation in industrial settings. Moreover, our model evinces robustness and efficacy in grappling with the intricacies entailed in the detection of minute capacitor defects compared with established models, such as SSD, Faster RCNN, YOLOv5n, and YOLOv7-tiny. This facet bears paramount significance in the sphere of electronic component fabrication, wherein the discernment of the tiniest imperfections assumes a pivotal role in assuring the reliability and performance of the entire system.