# Application Research of Improved YOLO V3 Algorithm in PCB Electronic Component Detection
## 4 Experiments and Analysis
### The Experimental Results
#### 4.3.3 Analysis of a Series of Curves  
To comprehensively compare and analyze the advantages and disadvantages of the four algorithms, we have drawn four curves for comparative analysis in Figure 13. Among the four curves, we use orange to represent YOLO V3 + COCO dataset 9 anchors algorithm, green for YOLO V3 + PCB dataset 9 anchors algorithm, red for YOLO V3 + PCB dataset 9 anchors + bbox800 algorithm, and blue for YOLO V3 + 4 outputs + PCB Dataset 12 anchors + bbox800 algorithm. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{4}{c}{**AP (Average Precision) of Algorithm**} \\ \cline{2-5}
**Category** & \begin{tabular}{c} **YOLO V3 +** \\ **COCO Dataset 9** \\ **Anchors** \\ \end{tabular} & \begin{tabular}{c} **YOLO V3 + PCB** \\ **Dataset 9 Anchors** \\ \end{tabular} & \begin{tabular}{c} **YOLO V3 + PCB** \\ **Dataset 9 Anchors** \\ \end{tabular} & \
\begin{tabular}{c} **YOLO V3 + PCB** \\ **Dataset 9 Anchors** \\ \end{tabular} &
\begin{tabular}{c} **YOLO V3 + PCB** \\ **Dataset 12** \\ **Anchors +** \\ **Bbox800** \\ \end{tabular} \\ \hline resistor & 0.41 & 0.41 & 0.38 & **0.48** \\ capacitor & 0.58 & 0.58 & 0.56 & 0.72 \\ test & 0.82 & 0.81 & 0.81 & **0.96** \\ unknown & 0.74 & 0.74 & 0.72 & **0.87** \\ emi & 0.91 & 0.93 & 0.94 & 0.99 \\ ferrite & 0.78 & 0.75 & 0.75 & **0.88** \\ pads & 0.55 & 0.53 & 0.55 & **0.74** \\ led & 0.88 & 0.89 & 0.87 & **0.94** \\ zener & 1 & 1 & 0.80 & **1** \\ component & 0.49 & 0.50 & 0.52 & 0.56 \\ transistor & 0.88 & 0.92 & 0.85 & 0.99 \\ diode & 0.80 & 0.83 & 0.82 & **0.94** \\ jumper & 0.71 & 0.77 & 0.75 & **1** \\ inductor & 0.74 & 0.75 & 0.62 & **0.96** \\ fuse & 0.59 & 0.51 & 0.44 & **1** \\ electrolytic & 0.67 & 0.65 & 0.53 & 0.99 \\ transformer & 1 & 1 & 1 & **1** \\ potentiometer & 0.71 & 0.54 & 0.61 & **1** \\ pins & 0.91 & 0.93 & 0.93 & **0.98** \\ clock & 0.60 & 0.61 & 0.55 & **1** \\ battery & 0.12 & 1 & 0.75 & **1** \\ button & 0.98 & 0.99 & 0.99 & **1** \\ ic & 0.74 & 0.77 & 0.75 & **0.98** \\ switch & 0.78 & 0.78 & 0.76 & **1** \\ connector & 0.94 & 0.96 & 0.95 & **1** \\ connector port & 1 & 1 & 1 & **1** \\ buzzer & 1 & 1 & 1 & **1** \\ heatsink & 1 & 1 & 1 & **1** \\ display & 1 & 1 & 1 & **1** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: AP for each electronic component category of four algorithms. Precision recall is a useful measure of success of prediction when categories are very imbalanced. In information retrieval, precision is a measure of result relevancy, while recall is a measure of how many truly relevant results are returned. With the accuracy of the \(y\)-axis and the recall rate of the \(x\)-axis, we get the precision-recall (P-R) curve. The P-R curve shows the tradeoff between precision and recall for different thresholds. The higher the accuracy, the higher the recall rate and our models and algorithms are more efficient. That is, the drawn P-R curve is as close as possible to the upper right. As shown in Figure 12(a), the blue line is closest to the upper right and encloses the other three algorithm curves. Therefore, the improved algorithm we finally propose is represented by the color blue and shows the best performance of the four algorithms. The mAP (mean average precision) provides a single-figure measure of quality across recall levels. Among evaluation measures of different target detection algorithms, mAP has been shown to have especially good discrimination and stability. In the experiments of this paper, the training steps have been iterated a total of 70,000 times. We have the mAP value as the \(y\)-axis and the range is from 0 to 100%; the number of iterations is the \(x\)-axis, which ranges from 0 to 70,000. We can see from Figure 12(b), when the number of training steps reaches 50,000, the best performing blue curve mAP has been stabilized, and the mAP maximum is 93.07%. This is the model selected in this paper. Figure 12(c) shows the loss value curve changes with iterations of the four algorithms.