# PEE: Prior-Enhanced and Resolution-Extended Object Detection for Industrial Laminated Panel Scenes
## 1 Introduction
The limited diversity in the dataset impedes the model's ability to effectively differentiate between regions of the image that share similar characteristics. Considering the possibility of deployment in industrial applications, the method of introducing prior information on a one-stage detector is more practical and attractive. To achieve this goal, the CNN detectors for industrial scenarios should learn to accurately and adaptively generate exact positive proposals of different objects at the right locations as well as avoid duplications. Therefore, we perform an analysis based on industrial scenes, as shown in Figure 1a. Among them, h1 represents the length of the panel in the entire scene, h2 represents the length of the entire image, and d represents the spacing between the steel bars in the scene. It can be seen that h1 is at least half of h2, and the value of d is fixed. The analysis reveals prior information available in the laminated panel scene. The boundaries of the laminated panels always occupy a large part of the original image, and there are the same intervals between the joints of the outer rebars. Prior information is then summarized through these obvious phenomena. It is concluded that the adjacent outer bars are always aligned horizontally or vertically and have almost the same spacing. Meanwhile, the area of a laminated panel is no more than 65 percent of the whole picture. A more intuitive presentation of the results is shown in Figure 1b, where 150 edge rebars are randomly selected in the examples and the distances between these examples are calculated in both the horizontal and vertical directions. Subsequently, we propose a prior-enhanced resolution-extended network for industrial scenarios (PERE). In PERE, we design relevant modules based on architectural modifications of a one-stage detector. To be more specific, a new framework, named the multi-class perceptual prior-enhanced network (MRPE), integrates prior information with industrial scenes to reduce misdetections of similar objects. This is achieved by introducing a scene graph and then restricting the spatial extent of each category during training. MRPE applies different restrictions based on known categories and enhances the feature performance by combining these limits with an improved attention module, reinforcing the detection efficiency. In addition, to address the poor representation of tiny objects, we propose a resolution-extended network (REN), which compensates for low-resolution objects by expanding feature maps at multiple scales. The feature is first expanded in horizontal and vertical directions and then pixels within the feature maps are shuffled into an extremely large feature map, and finally, new weights are generated for further feature fusion. REN increases the chances of tiny objects being noticed by the network, improving the accuracy of the localization. By combining the MRPE and REN modules with the initial network's aggregation, we achieve more reliable confidence scores for each category. Figure 1: The range of the laminated-panel bounding box is shown in the red rectangular box. Figure (**a**) represents the analysis of the spacing and length of objects within the scene, where h1 and h2 are the height of the laminated panel and the entire image, respectively, and d is the spacing between steel bars. Figure (**b**) represents the length statistics of the spacing between steel bars. Specifically, the contributions of this paper are as follows:  
* We propose a module of the multi-class perceptual prior-enhanced network (MRPE) for industrial scenarios, which generates prior information based on the object distribution to mitigate the object feature similarity-induced false detection problem. * We propose the extended resolution network (REN) module, which provides additional information compensation based on varying appearances and scales, improving tiny object detection quality.