# PEE: Prior-Enhanced and Resolution-Extended Object Detection for Industrial Laminated Panel Scenes
## 3 Materials and Methods
### General Overview
Since there are multiple object categories, this loss is computed using multivariate cross-entropy, which ensures the model classifies objects correctly. The coefficients \(\lambda_{\text{bbox}}\), \(\lambda_{\text{conf}}\), and \(\lambda_{\text{cls}}\) serve to balance the contribution of each loss term in the total loss function. These coefficients adjust the importance of each loss type based on the specific needs of the task. In our case, the values are set to 5, 1, and 1, respectively, where the higher weight for the bounding-box regression loss emphasizes the importance of precise localization of the objects. This balancing is essential for achieving good performance in object detection, as it ensures that the model focuses adequately on all three aspects: localization, detection confidence, and classification. In the original YOLOv9 architecture, the classification branch and the regression branch share the same structure. Specifically, YOLOv9 utilizes six interdependent detection branches, working together to produce the final detection results. In our approach, we retain most of the modules within these six detection branches, keeping them consistent with the primary YOLOv9 architecture. However, to enhance detection efficiency, especially in scenarios involving both large and extremely small objects, we introduce a novel framework called Multi-Category Perceptual Prior Enhancement (MRPE). This framework is designed to improve the detector's ability to perceive and differentiate between various categories by incorporating prior information. Additionally, MRPE enhances the features extracted from images, thereby improving overall detection accuracy. We replace some of the Adown modules in YOLOv9 with the MRPE module. While functional, these Adown modules have been found to negatively impact execution speed in industrial settings, making them less suitable for high-performance applications. Furthermore, using high-resolution input images has been shown to improve detection tasks in specialized datasets, particularly for identifying small-scale objects. However, directly introducing higher-resolution input images into the detection process significantly increases the computational load, which can degrade the overall performance of the detection algorithm. To address this challenge, we propose a resolution-extended network (REN), which integrates a super-resolution approach into the feature processing pipeline of the object detection model. REN achieves this while adding minimal computational overhead. In our implementation, we replace all the upsampling modules within the YOLOv9 network with REN. The original YOLOv9 upsampling relied on a nearest-neighbor interpolation strategy, which failed to consider contextual relationships within the image. In contrast, our REN module preserves these contextual relationships, leading to better feature resolution and, consequently, more accurate detection results. Detailed explanations of the MRPE and REN modules are provided in Sections 3.2 and 3.3, respectively.