# ETAFHrNet: A Transformer-Based Multi-Scale Network for Asymmetric Pavement Crack Segmentation
## 5 Results and Discussion
### Comparison with Existing Advanced Methods  
To validate the performance advantages of the proposed ETAFhrNet model in pavement crack detection, we conducted comparative experiments on the self-constructed QD-Crack dataset against several state-of-the-art segmentation models, including U-Net, DeepLabv3+, SegFormer, PSPNet, and HRNet. All models were trained and fine-tuned under identical experimental conditions to ensure fair comparison. As shown in Figure 11, each model was evaluated using three primary metrics: mean Intersection over Union (mIoU), F1-score, and mean Recall (mRecall). The results demonstrate that ETAFhrNet achieves an mIoU of 74.41%, representing a 10.92% improvement over HRNet. In addition, it attains an mPrecision of 83.84%, an mRecall of 84.51%, and an F1-score of 83.11%, highlighting the model's significant advantage in segmentation accuracy and overall performance [60]. Besides accuracy, ETAFhrNet also offers a balanced hardware footprint. With 50.6 M parameters, it is only 12% larger than the HRNet baseline yet 21% smaller than the Transformer-based SegFormer (64.0 M). Despite this mid-range size, ETAFhrNet delivers an mIoU that is 10.9 percentage points higher than HRNet and 5.2 percentage points higher than SegFormer, while achieving the highest throughput (28.56 FPS). This accuracy-capacity-speed triad makes it attractive for edge GPUs and NPUs that typically provide 8-16 GB of RAM. Furthermore, as presented in Table 5 and Figure 12, ETAFhrNet outperforms U-Net by 13.08% in mean Precision (mPrecision) and 13.28% in mean Recall (mRecall), indicating its superior overall segmentation performance. While DeepLabv3+ leverages atrous convolution to expand the receptive field, it exhibits limitations in modeling the continuity of slender cracks, resulting in a relatively low mIoU of 65.72%. SegFormer, constrained by its window partitioning strategy, tends to generate fragmented crack predictions during high-resolution detection tasks. Similarly, PSPNet, due to its coarse-grained context model  
Figure 11: Performance comparison (mIoU, F1-score, etc.) of models on the dataset. ing, demonstrates a higher omission rate in fine crack detection, achieving an mRecall of only 72.64%. Parameter-wise, all CNN baselines cluster between 31 M and 45 M, whereas SegFormer scales up to 64 M. ETAFHrNet falls between the two groups, indicating that its performance boost stems from architectural design rather than brute-force model scaling. Given this footprint, we further estimate the real-time capacity of ETAFHrNet over a typical pavement section. Assuming one image covers roughly 1.5 m of pavement, analyzing a 1 km segment requires about 667 images. At 28.56 FPS, the model can process these images in 23.4 s (excluding I/O), confirming its suitability for near-real-time mobile inspection. To provide a clearer illustration of the proposed model's performance benefits, we present a visual comparison using representative test samples, as shown in Figure 13. The figure presents original pavement images, ground-truth segmentation masks, and prediction results from ETAFHrNet, U-Net, and PSPNet, with red boxes marking key areas of discrepancy. The visual comparisons clearly show that ETAFHrNet offers superior performance in capturing directional and continuous crack features. In particular, for sample groups 1 and 4, ETAFHrNet accurately preserves crack continuity at junctions, where other models tend to produce fragmented outputs. In group 3, the model successfully detects  
Figure 12: Comparison of classification performance across different semantic segmentation models based on their confusion matrices.