Enhancing Edge Detection by Texture Handling Architecture and Noiseless Training Data (2407.19992v4)

Abstract: Image edge detection (ED) is a fundamental task in computer vision. While convolution-based models have significantly advanced ED performance, achieving high precision under strict error tolerance constraints remains challenging. Furthermore, the reliance on noisy, human-annotated training data limits model performance, even when the inputs are edge maps themselves. In this paper, we address these challenges in two key aspects. First, we propose a novel ED model incorporating Cascaded Skipping Density Blocks (CSDB) to enhance precision and robustness. Our model achieves state-of-the-art (SOTA) performance across multiple datasets, with substantial improvements in average precision (AP), as demonstrated by extensive experiments. Second, we introduce a novel data augmentation strategy that enables the integration of noiseless annotations during training, improving model performance, particularly when processing edge maps directly. Our findings contribute to a more precise ED architecture and the first method for integrating noiseless training data into ED tasks, offering potential directions for improving ED models. Codes can be found on https://github.com/Hao-B-Shu/SDPED.

• The paper introduces the SDPED model with cascaded skipping density blocks to significantly improve precision in edge detection tasks.
• It employs a novel noiseless data augmentation technique that refines training data and boosts accuracy under strict evaluation metrics.
• Experimental results show marked average precision gains across multiple datasets, challenging conventional evaluation benchmarks.

More Precise Edge Detections

The paper addresses fundamental issues in image edge detection (ED), a cornerstone of computer vision, by proposing a new model architecture utilizing cascaded skipping density blocks (CSDB). This novel approach aims to enhance the precision of edge detections, particularly under strict evaluation conditions, addressing limitations in current CNN-based models.

Contributions and Methodology

The paper's central contribution is the introduction of the SDPED (Skipping Density Precise Edge Detection) model, designed to improve the precision rates in ED tasks. The SDPED model employs CSDBs, which are optimized versions of previously successful density blocks used in tasks like image super-resolution. The proposed architecture does not rely on multi-scale supervision, challenging the prevailing assumption that multi-scale outputs are necessary for ED tasks.

Moreover, the paper introduces an innovative data augmentation technique that integrates noiseless data into the training process. This method arises from the observation that conventional human-annotated datasets inherently include noise, which negatively impacts model accuracy on edge maps. By treating labels as noiseless data during augmentation, the training dataset quality improves, enabling models to generate more precise edge predictions.

Experimental Results

The authors conducted extensive experiments on several datasets, including BRIND, UDED, MDBD, and BIPED2, under stricter evaluation criteria with a reduced error toleration distance. SDPED models exhibited state-of-the-art performance, particularly in average precision (AP) metrics, thus confirming their improved accuracy under the proposed high-standard benchmark.

The experimental results showed significant improvements:

• On BRIND, the SDPED model improved AP by approximately 6.9% compared to previous SOTA models.
• On UDED, the model recorded a 2.4% increase in AP.
• For MDBD, notable advancements were achieved with 22.5% improvement in AP under stricter tolerances.
• On BIPED2, while ODS and OIS scores were comparable to existing models, AP increased by 11.8%.

These results underscore the SDPED model's efficacy across various datasets and the robustness of its architectural innovations.

Theoretical and Practical Implications

The research shifts the perspective on model structure and training data quality in ED tasks. Architecturally, the elimination of down-sampling layers in favor of deep structures like CSDBs offers a fresh lens on achieving precision without multi-scale outputs. Practically, the modified data augmentation strategy provides a first approach to leveraging noiseless data, potentially transforming dataset curation practices.

Furthermore, setting a fixed pixel-based error toleration distance rather than a relative diagonal ratio offers a more consistent evaluation standard, which could prompt a reevaluation of existing benchmarks in ED research.

Limitations and Future Work

While the SDPED model demonstrates superior precision, the trade-off lies in its computational intensity and memory consumption, given its depth and operation on high-resolution inputs. Future work should focus on optimizing these aspects to balance precision with computational efficiency, potentially through model pruning or leveraging advanced hardware acceleration strategies.

Additionally, while the data augmentation approach offers a work-around for noiseless data, further research should explore methods to directly acquire high-quality edge data, possibly through automated annotation techniques or advanced data synthesis.

Conclusion

In summary, the paper presents a significant leap in precise edge detection, highlighting the interplay between advanced model architecture and data handling methods. By challenging existing paradigms, it opens new avenues for enhancing both the theoretical understanding and practical implementation of edge detection models. As ED remains crucial in numerous downstream tasks, these developments stand to impact a broad spectrum of computer vision applications.