# PEE: Prior-Enhanced and Resolution-Extended Object Detection for Industrial Laminated Panel Scenes
## 1 Introduction  
The laminated panel is one kind of industrial assembly element. With the advantage of low cost, high safety, and shorter construction cycles, it plays a pivotal role in the industrial construction field. However, laminated panels can hardly be directly assembled because of the difference in shape, direction, and other details between components within them. Meanwhile, specific information such as locations and categories of different items is vital for subsequent processes such as splicing and filtering. Therefore, the efficient localization and classification of the components within the laminated panels such as the truss and steel bars are crucial for practical deployment. Currently, the localization of components in the laminated panels relies deeply on manual methods, which are inefficient and labor-intensive for large-scale industrial production. At the same time, using vision algorithms to achieve automatic screening brings many benefits. Among these vision algorithms, optical-based detectors possess fewer parametersand more fitness for the tasks. Present optical-based object detectors can be categorized into two-stage detectors and one-stage detectors. At the expense of more handling time, two-stage detectors [1] first generate a large number of candidate frames and subsequently obtain more accurate results through post-processing. On the contrary, one-stage detectors [2; 3; 4] screen the image input directly and produce detection results quickly with the aid of predefined anchor frames. Therefore, the utilization of one-stage detectors benefits from the model's properties of quick handling and end-to-end processing, which suits industrial needs more nowadays. Therefore, one-stage object detectors are widely utilized in industrial object detection scenarios, such as lane detection tracking in industrial spots [5] and positioning, along with quality inspection of workshop accessories [6]. However, when applying one-stage detectors, the lack of adaptive model tuning and optimization for these unique tasks may cause bad detection performance. For instance, in tree detection, low contrast and lighting between the background and tree regions, with significant variance in the scales of trees, results in poor classification and localization. A simple solution to tackle this difficulty is to correlate the context between different categories by introducing local prior information to optimize the process of generating features. [PERSON] and [PERSON] have worked on improving pedestrian and cell detection, respectively, by using pipelines that include prior positional details [7; 8]. These methods use global information from HOG features or cellular features during training. While these approaches do lead to better results, they still struggle with detecting small objects with pixel dimensions less than \(20\times 10\) in a high-resolution image of \(4096\times 2180\). As a result, the accuracy of detection in these cases often falls short. CCNET addresses this issue by introducing criss-cross attention and gathering context in horizontal and vertical directions for every pixel in the feature map [9]. Some other existing methods have tried to improve this by applying techniques like improved channel-space dual-attention mechanisms, extra supervised branches, or joint training [10; 11; 12] to initial models. Inspired by these proven methods for specific industrial scenarios, we designed a new frame for laminated panels to reduce missed and false detections in industrial scenarios while improving detector performance. The frame fully utilizes the latent spatial distribution within the images, facilitating the performance of the origin features. By summarizing the main difficulties faced in this task, it can be concluded that industrial laminated panel scenes have a single texture and color tone, and there is a degree of similarity between different objects.