# Application Research of Improved YOLO V3 Algorithm in PCB Electronic Component Detection
## 1 Introduction
used a novel background removal algorithm and by appropriately combining complementary information sources (e.g., diverse color spaces, scales, etc. ), significantly improved localization performance in electronic components on PCB [12]. With the development of artificial intelligence technology, the use of deep learning to target detection of PCB electronic components can achieve the goal of robotic assembly in 3C manufacturing and production, and it has increasingly attracted the attention of researchers. [PERSON] et al. has established a PCB defect dataset and proposed a novel group pyramid pooling module to extract features of a large range of resolutions, which were merged by group to predict PCB defect of corresponding scales [13]. [PERSON] et al. produced and publicized a synthesized PCB dataset that has 1386 images with six types of common defects. Instead of simply superimposing the convolution layer, the authors used a dense shortcut from Densenet to achieve satisfactory defect detection with only a relatively small number of layers [14]. [PERSON] et al. were one of the first teams to try to detect common components on PCBs based on deep learning models, and they proposed a data set for common component inspection on PCBs and designed a novel Graph Network block to refine the component features conditioned on each PCB. Using the model SPN-T-W-GN-LF (Similarity prediction network with triplet loss and graph network), the mAP of electronic component detection on the testing of PCBs can reach 65.3% [15]. The previous two studies focused on the detection of a few types of electronic components in a simple scenario, although [15] used the embedded graph neural network to enhance feature implementation with the aim of detecting multi-target electronic components in a complex background, but since it is a three-stage pipeline, the problem of low efficiency and low robustness exists. We used the improved YOLO (you only look once) V3 [16] algorithm after the network structure changed based on the fast detection speed of a regression method to find electronic components. The detection accuracy significantly improved using virtual images for data enhancement and feature fusion based on different categories of training samples' scale. Figure 1 shows the technical flowchart of this study [17]. The contributions of this paper are summarized as follows:  
* We propose a PCB electronic component category training dataset that involves the virtual environment and real-world scenario collaboration and validate its effectiveness in a typical multi-category test set. * We use the K++ clustering algorithm to regenerate the anchors suitable for the distribution of PCB electronic components based on the size ratio of the target in the training dataset. * We design an improved YOLO V3 network structure and extend the exit of the YOLO layer by extracting the characteristics of the network layer suitable for small targets. This improved network structure is particularly suitable for detecting small SMD (Surface mounted devices) components on the PCB. The improved model's mAP can reach 93.07% of the PCB electronic components after comparison with the original YOLO V3. The rest of the paper is organized as follows: The improvements between YOLO V3 and the previous YOLO series are discussed in Section 2. Section 3 proposes an improved scheme based on YOLO V3 that can improve the target detection accuracy by analyzing the training dataset and test dataset of PCB electronic components. Section 4 provides the experimental results and analysis in detail. Section 5 concludes the paper and discusses future research issues. Figure 1: Technical flowchart of the study.