arXiv 2506.045262025-07-15cs.CV

EECD-Net：基于脉冲神经网络与门控注意力的节能型裂缝检测

EECD-Net: Energy-Efficient Crack Detection with Spiking Neural Networks and Gated Attention

Shuo Zhang

备注：: After further careful review and additional checks, we have identified multiple issues in our experimental results and data analysis that significantly affect the validity and reliability of our findings. We believe that these issues are substantial enough to compromise the scientific integrity of the manuscript

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AI总结：

本文提出EECD-Net多阶段裂缝检测方法，结合SRCNN、脉冲卷积单元和门控注意力Transformer，在CrackVision12K上达98.6%准确率且仅耗能5.6 mJ，实现高精度低功耗的道路监测。

摘要：

道路表面裂缝检测是仪器测量领域的一项关键技术，对确保基础设施安全和交通可靠性至关重要。然而，由于能源有限且成像分辨率低，智能终端设备难以维持实时监测性能。为克服这些挑战，本文提出了一种用于道路裂缝检测的多阶段检测方法EECD-Net，以提升仪器测量的准确性和能效。具体而言，采用复杂的超分辨率卷积神经网络（SRCNN）来解决低质量图像的固有难题，在保留关键结构细节的同时有效提升图像分辨率。同时，提出了带有连续整合发放（CIF）神经元的脉冲卷积单元（SCU），将这些图像转换为稀疏脉冲序列，显著降低功耗。此外，设计了门控注意力Transformer（GAT）模块，通过自适应注意力机制策略性地融合多尺度特征表示，有效捕捉长距离依赖关系和复杂的局部裂缝模式，显著增强了对不同裂缝形态的检测鲁棒性。在CrackVision12K基准上的实验表明，EECD-Net实现了98.6%的卓越检测准确率，以1.5%的显著优势超越了Hybrid-Segmentor等最先进方法。值得注意的是，EECD-Net保持了出色的能效，仅消耗5.6 mJ，相比基线实现大幅降低了33%。这项工作开创了基于仪器的裂缝检测的变革性方法，为资源受限环境下的实时、大规模基础设施监测提供了可扩展的低功耗解决方案。

英文摘要：

Crack detection on road surfaces is a critical measurement technology in the instrumentation domain, essential for ensuring infrastructure safety and transportation reliability. However, due to limited energy and low-resolution imaging, smart terminal devices struggle to maintain real-time monitoring performance. To overcome these challenges, this paper proposes a multi-stage detection approach for road crack detection, EECD-Net, to enhance accuracy and energy efficiency of instrumentation. Specifically, the sophisticated Super-Resolution Convolutional Neural Network (SRCNN) is employed to address the inherent challenges of low-quality images, which effectively enhance image resolution while preserving critical structural details. Meanwhile, a Spike Convolution Unit (SCU) with Continuous Integrate-and-Fire (CIF) neurons is proposed to convert these images into sparse pulse sequences, significantly reducing power consumption. Additionally, a Gated Attention Transformer (GAT) module is designed to strategically fuse multi-scale feature representations through adaptive attention mechanisms, effectively capturing both long-range dependencies and intricate local crack patterns, and significantly enhancing detection robustness across varying crack morphologies. The experiments on the CrackVision12K benchmark demonstrate that EECD-Net achieves a remarkable 98.6\% detection accuracy, surpassing state-of-the-art counterparts such as Hybrid-Segmentor by a significant 1.5\%. Notably, the EECD-Net maintains exceptional energy efficiency, consuming merely 5.6 mJ, which is a substantial 33\% reduction compared to baseline implementations. This work pioneers a transformative approach in instrumentation-based crack detection, offering a scalable, low-power solution for real-time, large-scale infrastructure monitoring in resource-constrained environments.

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