# Foreign-Object Detection in High-Voltage Transmission Line Based on Improved YOLOv8m
## 5 Experiment and Analysis
### Experimental Results and Analysis
#### 5.3.6 Detection Results for Different Classes  
Table 4 presents a comparative analysis of different models in detecting various foreign objects.  
From Table 4, it is evident that before data augmentation, our improved model (denoted as 'Ours') shows an overall increase in mean precision across all categories in terms of mAP_0.5, mAP_0.5:0.95, and recall when compared to the original YOLOv8m model. However, there is a decrease in precision. Specifically, for mAP_0.5, the 'twig' category exhibits the most significant improvement, with other categories also showing improvements or remaining relatively stable. Similarly, in mAP_0.5:0.95, 'twig' demonstrates the most substantial advancement, while the other categories also display enhancements or stability. Regarding precision, except for 'twig' and 'balloon', which experience improvements, the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Foreign Object**} & \multirow{2}{*}{**Labels**} & \multirow{2}{*}{**mAP\_0.5 (\%)**} & **mAP\_0.5:0.95** & **Precision** & **Recall** \\  & & & & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline \multirow{8}{*}{YOLOv8m} & all & 1008 & 92.8 & 76.4 & 98.4 & 85.9 \\  & nest & 514 & 98.7 & 85.9 & 99.4 & 93.3 \\  & trash & 134 & 98.4 & 84.9 & 100 & 92.5 \\  & kite & 45 & 86.3 & 70.1 & 99.2 & 71.1 \\  & twig & 14 & 76.1 & 37.9 & 93.2 & 64.3 \\  & bird & 242 & 99.5 & 89.6 & 99.6 & 99.2 \\  & balloon & 59 & 97.4 & 89.9 & 99.3 & 94.9 \\ \hline \multirow{8}{*}{Ours} & all & 1008 & 95.5 & 80.4 & 96.7 & 91.9 \\  & nest & 514 & 98.7 & 87.3 & 98.6 & 96.3 \\  & trash & 134 & 98.7 & 88.4 & 98.2 & 94.8 \\  & kite & 45 & 87.9 & 75.8 & 87.3 & 80 \\  & twig & 14 & 90.4 & 48.3 & 97.1 & 85.7 \\  & bird & 242 & 99.5 & 90.1 & 99.4 & 99.6 \\  & balloon & 59 & 97.6 & 92.8 & 99.4 & 94.9 \\ \hline \multirow{8}{*}{Augmentation} & all & 2798 & 97.4 & 86.4 & 98.6 & 94.1 \\  & nest & 1004 & 99.1 & 88.4 & 99.8 & 98 \\ \cline{1-1}  & trash & 255 & 93.9 & 86.1 & 97.8 & 90.5 \\ \cline{1-1}  & kite & 457 & 96.1 & 81.5 & 97.1 & 90.3 \\ \cline{1-1}  & twig & 246 & 99.3 & 82.3 & 98.4 & 97.1 \\ \cline{1-1}  & bird & 487 & 99 & 90.4 & 98.9 & 97.2 \\ \cline{1-1}  & balloon & 349 & 96.9 & 89.7 & 99.4 & 91.7 \\ \hline \multirow{8}{*}{Augmentation + Ours} & all & 2798 & 99 & 89.5 & 99.6 & 96.8 \\ \cline{1-1}  & nest & 1004 & 99.4 & 89.8 & 99.5 & 97.8 \\ \cline{1-1}  & trash & 255 & 98 & 91 & 99.7 & 94.5 \\ \cline{1-1}  & kite & 457 & 98.1 & 87.1 & 98.8 & 92.3 \\ \cline{1-1}  & twig & 246 & 99.5 & 84.7 & 100 & 97.5 \\ \cline{1-1}  & bird & 487 & 99.4 & 91.8 & 99.4 & 99 \\ \cline{1-1}  & balloon & 349 & 99.5 & 93 & 100 & 99.4 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Detection Results of Various Foreign Objects.  
precision of other categories decreases. For recall, all categories witness an increase or remain stable.  
Following data augmentation, a comparison between our improved model ('Ours') and the original YOLOv8m model reveals an overall increase in mean precision across all categories concerning mAP_0.5, mAP_0.5:0.95, precision, and recall. In mAP_0.5, the precision of each category rises. In mAP_0.5:0.95, the precision of each category shows improvement. Regarding precision, 'nest' experiences a decline, but the precision of other categories improves. In recall, 'nest' shows a decline, but the precision of other categories sees an increase.