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

TL;DR

EECD-Net is a novel energy-efficient crack detection framework that combines super-resolution, spiking neural networks, and attention mechanisms to improve accuracy and reduce power consumption for infrastructure monitoring.

Contribution

This paper introduces EECD-Net, integrating SRCNN, SCU with CIF neurons, and GAT modules to enhance crack detection accuracy and energy efficiency in low-resource environments.

Findings

Achieves 98.6% detection accuracy on CrackVision12K

Consumes only 5.6 mJ, 33% less energy than baseline

Outperforms state-of-the-art methods in accuracy and efficiency

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.