# Application Research of Improved YOLO V3 Algorithm in PCB Electronic Component Detection
## 5 Conclusions  
With the improvement of the manufacturing process, materials and technology of electronic components, the appearance of electronic components has the characteristics of different shapes and large changes in size. Vision-based detection of electronic components on PCBs is a core issue in the intelligent manufacturing of electronic products. At present, the use of deep learning to detect PCB electronic components is mainly concentrated in several cases with few types and quantities, and the accuracy is not high. Studying how to use efficient and fast target detection methods to detect a large number of electronic components on a PCB can provide more possibilities for defect detection and robot assembly of electronic products. In this study, we used YOLO V3, which has good detection speed and good detection results, as the basic framework for electronic component detection on PCB. Firstly, it provides a real PCB photo and a virtual PCB photo with synthetic data as a joint dataset. The synthetic data method provides label information for as many electronic components as possible. Secondly, we analyzed the sample size distribution in the training set and studied the output results of five YOLO V3 network structure dimensionality reduction operations. We found that each pixel represents the different sizes of the original image. This gives us the idea of selecting feature layers according to the size of different detection target categories to improve the detection accuracy. Finally, we generated some new anchor boxes based on the clustering results of PCB electronic components in the training set. Based on the above data, we verified through a series of experiments that the final improvement model increased the mAP of the original YOLO V3 from 77.08% to 93.07%. Although the final improvement algorithm does improve the overall detection accuracy of electronic components on the PCB, we also found that the AP (average precision) of the responder, capacitor and component was 0.41, 0.58 and 0.49, respectively, in the original YOLO V3. The AP after \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{\begin{tabular}{c} **SSD** \\ **(single-shot** \\ **multi-box** \\ **multi-box** \\ **detectors)** \\ \end{tabular} } & \begin{tabular}{c} **YOLO V3 +** \\ **COCO** \\ **Dataset 9** \\ **Anchors** \\ \end{tabular} & \begin{tabular}{c} **YOLO V3 +** \\ **PCB Dataset** \\ **9 Anchors** \\ **Bbox800** \\ \end{tabular} & \
\begin{tabular}{c} **YOLO V3 +** \\ **4 Outputs +** \\ **PCB Dataset** \\ **12 Anchors** \\ **+ Bbox800** \\ \end{tabular} \\ \hline \hline \multirow{2}{*}{
\begin{tabular}{c} mAP (\%) \\ \end{tabular} } & 80.25 & 83.16 & 77.14 & 79.88 & 76.43 & 93.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: mAP and detection speed for some object detection methods on the PCB dataset. the improved algorithm was 0.48, 0.72, and 0.56, respectively. That is, the increase in detection accuracy was small. The problem of resistor and capacitor detection is that the appearance is similar and the variance between categories is small. The detection problem of components is that the characters on the IC are different, and the variance within the category is large. It is these findings that allow us to explore further. As [PERSON] et al. proposed, deep learning can also be used to implement scenario understanding so that we can fully consider the context of electronic components on the PCB [28]. We will perform the detection of the resistor and capacitor according to the silkscreen printing on the PCB. The detection of the component can also be realized by the edge detection method in the IC area. In the process of studying the YOLO V3 model and improving its algorithm, the most unanticipated finding was that the YOLO algorithm completely provides an open structure for various target detection tasks. We can completely detect the target feature based on the original YOLO network by cropping, deepening, lightening, combining and determine the number of output layers.