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

• URL: http://arxiv.org/abs/2506.04526v3
• Date: Tue, 15 Jul 2025 08:13:54 GMT
• Title: EECD-Net: Energy-Efficient Crack Detection with Spiking Neural Networks and Gated Attention
• Authors: Shuo Zhang,
• Abstract summary: This paper proposes a multi-stage detection approach for road crack detection, EECD-Net, to enhance accuracy and energy efficiency of instrumentation.<n>Super-Resolution Convolutional Neural Network (SRCNN) is employed to address the inherent challenges of low-quality images.<n>A Spike Convolution Unit (SCU) with Continuous Integrate-and-Fire (CIF) neurons is proposed to convert these images into sparse pulse sequences.<n>Experiments on the CrackVision12K benchmark demonstrate that EECD-Net achieves a remarkable 98.6% detection accuracy.
• Score: 6.723950151272841
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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