# PEE: Prior-Enhanced and Resolution-Extended Object Detection for Industrial Laminated Panel Scenes
## 3 Materials and Methods
### Multi-Class Perceptual Prior Enhanced Networks  
In inspection tasks, the classification subtask focuses on finding a way to distinguish between the category being inspected and other background elements. For example, in a laminated panel inspection project, this means identifying categories like reinforcement bars, outer reinforcement bars, and other backgrounds such as the panel boundary. The main challenges here are dealing with dense occlusions and the small size of objects. Additionally, in traditional object detection tasks, features from different locations and scales do not equally contribute to the final result. This, along with the fact that much of the regular pattern information in a specific scene is often overlooked, leads to the network not fully utilizing important features. These issues lead to varying levels of false detections, which can become more pronounced when using single-stage object detectors. This is because single-stage detectors omit the candidate anchor frame step, leading to less accurate results. To tackle these challenges, a common strategy is to use fine-tuned attention modules. This approach aims to enhance the efficiency of global information usage by adjusting the focus of the neuron's receptive field. However, this solution is not always effective for laminated panel detection tasks. The attention mechanism's performance must be validated through extensive experiments  
Figure 2: General overview of prior-enhanced resolution-extended object detector for industrial scenarios. The structure is based on the modular architecture of YOLOv9 and includes a main detection branch and an auxiliary detection branch. We designed and implemented the MRPE module to replace the original Adown module in the auxiliary branch. The MRPE module is placed between the YOLOBackbone and YOLONeck. Additionally, we replaced the original upsampling module with the new REN module, where \(\alpha\) represents the upsampling multiple, and the multiple is set to 2 in our experiments. In this system, the input image is first processed by the YOLOBackbone to extract basic image features. These features are then refined by the MRPE, YOLONeck, and REN modules, leading to more accurate detection results, which are used for subsequent tasks. with different models and datasets. In practice, mainstream attention-based methods have not shown significant improvements for tasks like laminated panel detection. Specifically, conventional attention modules have difficulty managing the large pixel gap between categories such as long rebar and line. And in some cases, these categories can even conflict with each other. As a result, regardless of merging the idea of attention, the detector often remains focused on localized information. The limitation can lead to false detections of objects with similar features, reducing the overall accuracy and performance of the detector. The persistent reliance on localized information means that important contextual features are not fully utilized, further impacting the detector's effectiveness in distinguishing between different categories in complex scenarios. Considering the above problems, we propose exploring more effective attention modules to guide the model towards better localization. Inspired by the concept of scene graph enhancement used in recurrent neural networks and the idea of utilizing prior information, we aim to improve feature representation by creating and aligning a scene graph with the training data. By integrating prior information and attention mechanisms, we enhance the model's ability to capture and utilize key features. Our approach involves generating prior information specific to the laminated panel scenario to assist in feature extraction, as illustrated in Figure 3. The architecture includes a main feature processing module and an auxiliary branch.