Abstract
Recent advancements have demonstrated the effectiveness of the extractor-selector (E-S) framework in edge detection (ED) tasks, which achieves state-of-the-art (SOTA) performance in both quantitative metrics and perceptual quality. However, this method still falls short of fully exploiting the potential of feature extractors, as selectors only operate on highly compressed feature maps that lack diversity and suffer from substantial information loss. Additionally, while union training can improve perceptual quality, the highest evaluation scores are typically obtained without it, creating a trade-off between quantitative accuracy and perceptual fidelity. To address these limitations, we propose an enhanced E-S architecture, which utilizes richer, less-loss feature representations and incorporates auxiliary features during the selection process, thereby improving the effectiveness of the feature selection mechanism. Additionally, we introduce a novel loss function, the Symmetrization Weight Binary Cross-Entropy (SWBCE), which simultaneously emphasizes both the recall of edge pixels and the suppression of erroneous edge predictions, thereby enhancing the predictions both in the perceptual quality and the prediction accuracy. The effectiveness and superiority of our approaches over baseline models, the standard E-S framework, and the standard Weight Binary Cross-Entropy (WBCE) loss function are demonstrated by extensive experiments. For example, our enhanced E-S architecture trained with SWBCE loss function achieves average improvements of 8.25$\%$, 8.01$\%$, and 33.25$\%$ in ODS, OIS, and AP, measured on BIPED2 compared with the baseline models, significantly outperforming the standard E-S method. The results set new benchmarks for ED tasks, and highlight the potential of the methods in beyond.
Abstract (translated)
最近的研究进展展示了提取器-选择器（E-S）框架在边缘检测（ED）任务中的有效性，该框架在量化指标和感知质量方面均达到了最先进的性能。然而，此方法仍然未能充分利用特征提取器的全部潜力，因为选择器仅操作于高度压缩且缺乏多样性的特征图上，并因此导致了大量信息损失。此外，尽管联合训练可以提高感知质量，但通常最高评估分数是在没有联合训练的情况下获得的，这就造成了定量准确性与感知保真度之间的权衡。为了解决这些限制，我们提出了一种增强版E-S架构，该架构利用更为丰富、丢失较少的信息特征表示，并在选择过程中加入辅助特征，从而提高了特征选择机制的有效性。此外，我们还引入了一种新的损失函数——对称化权重二元交叉熵（SWBCE），它同时强调了边缘像素的召回率和错误边缘预测的抑制，从而提升了感知质量和预测准确性。 通过广泛的实验，我们的方法在基线模型、标准E-S框架以及标准加权二元交叉熵（WBCE）损失函数方面展示了其有效性和优越性。例如，在BIPED2数据集上与基线模型相比，使用SWBCE损失函数训练的增强版E-S架构分别实现了ODS、OIS和AP指标平均提高了8.25%，8.01% 和33.25%，显著优于标准E-S方法的表现。这些结果为边缘检测任务设立了新的基准，并突显了该方法在更广泛应用中的潜力。
URL
https://arxiv.org/abs/2501.13365
