## 1 Introduction  
Robot vision automatic assembly technology dramatically improves the production efficiency of industrial assembly lines. Electronics factories require a high production rhythm. Low labor involvement and high automation will help manufacturers increase production efficiency. In the automatic assembly of liquid crystal display (LCD) TV modules, the assembly of diffuser plates (DPs) is necessary. LCD TVs generally comprise two main parts, namely the backlight module (BLM) and LCD module (LCM). Because most LCMs cannot emit light themselves, the BLM undertakes the role of lighting the LCD. As shown in Figure 1, a common BLM mainly comprises a back plate, light source strips, a light guide plate, and a piece of DP from bottom to top. In the assembly process, robots placed at different stations on the assembly line must complete tasks such as placing the back plates, assembling light source strips, placing the light guide plates, and assemblingthe DPs. When assembling a piece of DP on a specific BLM, the group of vacuum suction cups mounted on the end effect of the robot blindly grasps the DP placed at the fixed feed position and moves directly below the cameras for vision-based localizing. In order to ensure sufficient accuracy, each of the multiple cameras shoots a local corner of the DP, and together, they estimate the pose. A DP is a flat film made of polymethyl methacrylate material with some roughness on the surface, and has the effect of diffusing light. The light will scatter on the surface, spreading softly and evenly. This can be approximately considered to conform to the Lambert diffuse model. A DP can be considered a typical texture-less object. When the vision system begins detecting, the observed foreground comprises a gray-scale homogenous surface and a right-angled edge with low gradient amplitude. As the foreground overlaps the back plate and superimposed optical film, many classical vision detection methods show poor robustness and low locating accuracy. Classical texture-less object detection methods can be divided into template-based matching and learning-based according to the method of obtaining features. The learning-based method generally automatically extracts features that can be used online through a large sum of training images. In contrast, template-based matching methods mainly use handcrafted features to build templates that participate in matching. There are two main types of learning-based methods: traditional machine learning and deep learning. Hough forest [1, 2], as a machine learning method, integrates Random Forest [3] and Hough voting. The image patch representations employed by this method are typically based on descriptors like edge filters, or HOG [4], which have limitations in detecting texture-less surface features. The Deformable Part Model (DPM) [5] represents an object as a configuration of multiple flexible parts. It comprises a root filter and several part models, which undergo processing by the SVM [6] classifier. While this method excels in handling deformed targets, it may not be the most optimal for diffuser plates. Deep learning methods, such as RetinaNet [7] and YOLO [8, 9, 10], are convolutional neural network (CNN)-based object detection frameworks. RetinaNet takes ResNet [11] as its backbone and has a Feature Pyramid Network (FPN) structure [12], and can merge multi-scale features together so that it can perform well in detecting objects of different sizes. YOLO is a series of classical one-stage object detection approaches; each version focuses on lightweight design and excellent engineering, making it especially useful in industrial applications. However, it is a single-stage detector with relatively poor localizing accuracy. Compared with CNN-based methods, transformer-based methods such as Vision Transformer (VIT) [13] and Swin Transformer [14] usually have higher model complexity and slower training speed.