# Application Research of Improved YOLO V3 Algorithm in PCB Electronic Component Detection
## 1 Introduction
The algorithm first performs region proposal extraction on the detected images. Region proposal uses the texture, color and other information in the image, so the number is small, and the quality is high. It also uses the deep neural network to automatically extract and classify these candidate windows. Then it merges the regions that contain the same target, and finally outputs the target region we want to detect. Such algorithms are represented by RCNN (Regions with convolutional neural network features), Fast RCNN, Faster RCNN, SPP-net (Spatial pyramid pooling), and R-FCN (Region-based fully convolutional networks). [PERSON] et al. proposed a real-time small-flow symbol detection method based on revised Faster RCNN. Firstly, the small region proposal is used to extract the characteristics of small traffic signs, and then the revision structure of Faster RCNN combines the online hard instance mining. The combination can effectively detect small and medium traffic signs in surveillance video or driving recorder videos [7]. [PERSON] et al. proposed a cascaded network to identify gestures in complex scenes. First, they used a region-based complete convolutional neural network (R-FCN), which detects small objects, detects gestures, and then used the online hard instance test (OHET) to select hard instances to further improve the accuracy of gesture recognition. The VGG-19 (Visual geometry group-19)classification network reclassifies hard instances to obtain the final output of the gesture recognition system; the latest results have achieved a 99.3% mean average precision (mAP) of small and similar gestures in complex scenes. scenes [8]. The deep learning regression algorithm that is the one-stage method is represented by YOLO (you only look once), SSD (single-shot multi-box detectors), etc. This kind of target detection algorithm discards the process of generating regional proposal and directly returns the position and category of the bounding box in the output layer, which greatly improves the detection speed. [PERSON] et al. did not need to extract features manually, using two ZiYuan3 (ZY3) remote sensing images to create six training data sets with different band combinations and sliding window sizes, and training single-shot multi-box detectors (SSD) models, respectively. This proposed method obtains a higher rate of identification of poppy plots [9]. [PERSON] et al. proposed a multi-view YOLO object detection method, model structure and working method, which improved the ability to detect small objects. Compared with the two-stage method, this multi-view one-stage method is faster when it reaches the same map when implementing small object detection [2]. Vision-based inspection of electronic components on PCB can solve the problem of defect detection in 3C manufacturing. However, due to the wide variety and variety of electronic components, it has always been an industry problem. [PERSON] et al. found typical PCB electronic components, including protective coatings, markings and solder joints, proposed the detection of solder joints based on specular properties and the determined the specular interval of gray levels by the multilevel thresholding algorithm while passing the solder joint color. The distribution features are compared with the color distribution features of the reference object to identify and remove marks, via and other ineffective specular reflection regions, ultimately enabling automatic detection of multiple electronic components in the PCB image [10]. The electronic components detected by [PERSON] et al. were transistors, capacitors and diodes. Therefore, a knowledge-based method is proposed for target detection, that is, first it determines the aspect ratio and the pin number of the component, then it calculates the roundness of the device, and finally achieves the detection target of the electronic component through correlation matching [11]. [PERSON] et al.