# Application Research of Improved YOLO V3 Algorithm in PCB Electronic Component Detection
## 1 Introduction  
The electronic information industry has the characteristics of high technology content, high added value and low pollution. In addition to traditional digital cameras, mobile phones, computers, and other 3C (Computer, Communication and Consumer Electronics) products, emerging 3C products have emerged in recent years: smart TVs, smartwatches, smart wearable devices, drones, sweeping robots, entertainment robots, etc. With the further development of market hotspot products represented by flat-panel TVs and smartphones, the electronic information industry has become increasingly influential in social change and is regarded as a strategic development industry by major countries around theworld. The traditional 3C industry is a labor-intensive industry, and labor is one of the major costs of 3C manufacturing companies. At the same time, the new generation of labor is reluctant to engage in repetitive work that is boring with low added value, causing the 3C manufacturing industry to face a serious labor shortage. Based on the above situation, 3C terminal manufacturers must improve production automation by reducing labor shortages, improving productivity, enhancing competitive advantage, and completing industrial transformation and upgrading. That is, 3C manufacturing is shifting from labor-driven to equipment-driven. With the advent of the industry 4.0 era, the role of machine vision in the 3C manufacturing field is becoming more and more important. According to the application function, it is mainly reflected in four aspects: measurement, detection, identification and positioning, especially PCB (Printed circuit board) electronics based on intelligent vision technology. Component detection is an important step in PCB automation production monitoring and hardware reverse engineering. PCB electronic component detection belongs to the target detection category. Target detection is usually done in two steps in the literature: (i) the possible position of the target in the video or picture of the recognition object is fed back from the camera, and (ii) classifying the signs for a given cropped image where only a sign (or maybe nothing relevant) is visible, determining whether it is a sign or not, and what sign it is [1]. That is, target detection can be understood as a combination of object recognition and object location [2; 3]. Various target detection visual algorithms have been evolving over the past three decades. At present, the target detection algorithms appearing in academia and industry are mainly divided into three categories: traditional object detection algorithm, two-stage method, and one-stage method. Traditional target detection is mostly based on statistics or knowledge. Its characteristic attributes rely on manual design, and the detection objects are relatively limited. The face and license plate are the main ones. The algorithm is based on cascade + Harr/SVM (support vector machine) + HOG (histogram of oriented gradient)/DPM (deformable part model), and its improvement and optimization algorithms. [PERSON] et al. proposed a detection algorithm based on Harr and directional gradient histogram (HOG) features. The algorithm makes full use of the HOG feature of the target vehicle and uses the Harr feature to extract the foreground area (ROI, Region of interest). The AdaBoost classifier of the joint structure performs feature and target area classification and detects and tracks multiple vehicle targets successfully in a complex urban environment [4]. [PERSON] et al. proposed a multi-resolution layered component model and corresponding coarse-to-fine inference process which can be cascaded with the deformable part to multiply the acceleration factor to achieve accelerated object detection [5]. However, deep learning is gradually becoming the mainstream in target detection with its enormous success beginning with the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) of 2012 [6]. The two-stage method is also called the deep learning classification algorithm.