# PEE: Prior-Enhanced and Resolution-Extended Object Detection for Industrial Laminated Panel Scenes
## 4 Results
### Ablation Experiment  
We perform an ablation analysis of all components of the proposed model on a dataset captured by a matrix camera and manually segmented. We continue to use YOLOv9 as the base detector in all ablation experiments and evaluate the effect on the validation set. To evaluate the impact of our proposed MRPE (Multi-class Perceptual Prior Enhancement) and REN (Resolution Enhancement Network) modules, we conducted an ablation study. The results, summarized in Table 1, highlight the contributions of each component to the performance of our detection model. Initially, we trained a baseline detector using YOLOv9-c without the MRPE and REN modules. This baseline model applied one-to-one label assignment directly and did not include the additional multi-class perceptual prior enhancement or upsampling processing. We then integrated the MRPE module into the HEAD layer of the network. This enhancement led to a notable improvement of 4% mAP@50 in the line category and a 0.4% mAP@50 increase in the rebar category. Next, we replaced the upsampling module in the baseline detector with the REN module. This adjustment allowed us to create multi-resolution feature splicing by combining the original output branches with those from the REN. This configuration resulted in a 1% mAP@50 gain for the line category and a 0.6% mAP@50 gain for the rebar category compared to the baseline model. When both the MRPE and REN were incorporated into the model pipeline, we observed significant overall improvements. Specifically, the full model achieved a 4.7% mAP@50 gain for the line category, a 0.8% mAP@50 gain for the outer rebar category, and a 0.4% mAP@50 gain for the rebar category. Notably, the addition of only the  
Figure 6: A qualitative comparison of YOLOv5 and PERE, which are relatively on the left side and right side of the picture. REN module resulted in the best performance for the rebar category. This is due to the REN and MRPE creating some redundancy in detecting objects with extreme aspect ratios. Interestingly, while adding the MRPE and REN individually did not significantly shift the accuracy for the outer rebar category, combining both modules led to a substantial 0.8% mAP@50 gain. This improvement is attributed to the enhanced sensitivity of the MRPE and REN to small-scale objects when used together, confirming the effectiveness of our proposed model enhancements. In summary, the ablation study demonstrates that both the MRPE and REN contribute to improved detection performance, with their combined use providing the most substantial gains across the evaluated categories. The architecture of the MRPE is as follows. The different architectures of the MRPE are investigated in Table 6, using the YOLOv9 detector alone as a baseline, and the results are also shown in it, where MRPE s1 denotes the approach that utilizes only a single layer of modules and does not bring in prior information, MRPE s2 denotes the approach that utilizes only a single layer of attention block and brings in prior information, and MRPE s3 denotes the approach that utilizes two layers of attention blocks and brings in prior information as designed. As can be seen from the table, our proposed MRPE with multiple cascade structures shows significant improvement over the baseline detector in the smaller pixel classes, which suggests that our strategy of applying multi-class perception before enhancing feature alignment can help the model to pay more attention to the small-scale objects when fusing features and thus improve the detection efficiency of the small objects in the scenarios with large gaps between classes. Specifically, the best performance is obtained using MRPE blocks with a block number of 2; this suggests that two refinements of the features are sufficient and that more frequent feature aggregation makes the network redundant. The architecture of REN is as follows. The different architectures of the REN are investigated in Table 7, where the YOLOv9 detector alone is used as a baseline.