# Solder Joint Defect Detection in the Connectors Using Improved Faster-RCNN Algorithm
## 1 Introduction  
Recently, the rapid development of various industries has brought earth-shaking changes to the world, especially the electronic products industry. Electronic products are getting smaller and smaller, with more and more parts, and more and more powerful functions. Therefore, it is very important to check the quality of electronic products to ensure their high accuracy, stability, and safety. Currently, most electronic products use all kinds of connectors to complete power supply and information transmission. The connector is mainly connected to the cable by welding. The welding quality directly determines the ability of the connector cable, one of the important test indicators [1]. The earliest method of solder joint quality inspection for connectors was manual inspection. However, in this method, workers are prone to eye fatigue and other uncontrollable situations during the detection process, resulting in low detection efficiency and even false detection and omission. Once the above situation occurs, it will greatly reduce the service life of the cable, and even cause a short circuit of the cable due to disconnection and other phenomena, resulting in serious industrial accidents. In order to eliminate such hidden dangers, experts and scholars have successively developed automatic detection systems, committed to replacing people with instruments for completing the boring and repetitive work of cable welding defect detection, and proposed a series of detection methods, such as resistance measurement method [2], voltage drop measurement method andX-ray detection method [3]. However, these methods have not been widely used in the industrial field because of low detection accuracy and high cost. With the development of cameras and computers, digital image quality is getting higher and higher, and computers are becoming more and more capable of image processing. Scholars have found that detection of welding defects by image processing can achieve higher accuracy and efficiency, thus an automatic optical inspection (AOI) system [4; 5; 6; 7; 8] was proposed. Since all solder joints on the printed circuit board (PCB) are fixed, with quite different solder color, brightness, and other features from the background plate, it is relatively easy to the corresponding algorithm to extract the solder joint features, so as to analyze the solder joint quality. Therefore, AOI has received much attention in the task of PCB solder joint detection. [PERSON] et al. designed an automatic optical inspection algorithm to extract the position, shape and logic characteristics of PCB solder joints, and then successfully identify various welding defects [9]. Aiming at the low efficiency and accuracy of traditional PCB solder joint detection, [PERSON] et al. proposed a PCB solder joint detection method based on automatic threshold segmentation algorithm and image shape feature extraction algorithm, which improved the efficiency and accuracy of PCB solder joint detection [10]. However, there are some differences between connector solder joints and PCB solder joints. First of all, all components on the PCB have the corresponding location of the welding spot, easy to automatic welding. While the connector usually does not need to weld all the pins, so the welding process requires more human intervention, with more uncertain solder joint features. Secondly, the connector uses tin to weld the core wire to a pin cup. The color of tin is similar to the pin color of most connectors, which is easy to be interfered by environmental factors in the image, so it is not conducive to extracting effective solder joint features. Therefore, AOI cannot be used for connector solder joint detection as a PCB. In 2012, [PERSON]'s team participated in the ImageNet image recognition competition for the first time, and won the first prize by using convolutional neural network AlexNet [11]. Since then, the convolutional neural network has attracted worldwide attention on the problem of target classification.