# PEE: Prior-Enhanced and Resolution-Extended Object Detection for Industrial Laminated Panel Scenes
## 4 Results
### Laminated Panel Datasets and Evaluation Indicators  
For this study, we focus on detecting objects in laminated panels, and we created a dataset using images from such environments. To be more specific, the laminated panels in this study are made of metal. Using a matrix camera, we captured approximately 7500 images of industrial laminated panel scenes. For practical deployment, we selected 2888 images from these captured images to form our dataset. One of the images contains at least one laminated panel, and in some specific cases, multiple images capture the same panel from different angles. Therefore, after statistical analysis, a total of 1300 different laminated panels are included. These images represent a variety of conditions, including varying lighting levels, different textures of laminated panels, and different object positions. The dataset is divided into three parts: 2304 images for training, 292 images for validation, and 292 images for testing, strictly following the 8:1:1 ratio for dataset splitting. This ensures that the validation and test sets are distinct from the training data. We have named the dataset Laminated Panel Images (SPI). To further challenge the detection model, we categorized the images based on difficulty and ensured that the test set included as many difficult-to-detect samples as possible. However, only a small portion of these harder samples were used during training. A representative sample of SPI is shown in Figure 5. The SPI dataset is accessible at [[https://huggingface.co/datasets/shiningfrog/laminated_panels](https://huggingface.co/datasets/shiningfrog/laminated_panels)]([https://huggingface.co/datasets/shiningfrog/laminated_panels](https://huggingface.co/datasets/shiningfrog/laminated_panels)) (accessed on 10 April 2025). To speed up the model's inference without sacrificing image quality, we resized the images to 4096 \(\times\) 2180 pixels and converted them from BMP to JPG format. To evaluate the performance of our model on the industrial laminated panel detection task, we use a set of well-defined metrics that capture both accuracy and efficiency. We initially labeled our dataset with nine distinct categories but chose to focus on three representative ones: center, edge rebar, and rebar. These categories were selected to simulate extreme scale gap scenarios that are common in real-world applications. Center refers to the central portions of the rebar, which are typically much smaller in pixel size compared to the rebar itself. Edge rebar denotes the rebar at the image edges, often featuring small pixel sizes and curved hooks that indicate protrusions from the concrete slab. Rebars generally have extreme aspect ratios and can occupy significant portions of the image both horizontally and vertically. Given the characteristics of these categories, the evaluation metric average precision at 50% intersection over union (mAP@50) was selected. This metric measures the accuracy of the model at a specific IoU threshold of 50%, which aligns well with the needs of the project and provides a clear view of the model's performance across different categories. Using AP@50 instead of broader metrics like AP or mAP@50-95 allows for more granular insights into category-specific performance, facilitating effective experimental comparisons. In addition to precision, recall (R) is crucial for assessing the model's ability to identify all relevant objects, representing the proportion of true positive detections to the total number of actual positive samples. This metric helps evaluate how well the model detects objects across all categories. The metric Giga Floating-Point Operations per Second (GFLOPs) is used to evaluate the computational efficiency of the model. This parameter is critical for assessing the feasibility of deploying the model in real-world industrial settings where computational resources may be limited. To summarize, the evaluation metrics for this experiment include recall (R), mean average precision at 50% IoU (mAP@50), and GFLOPs.