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
• 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.

Related papers

• CEM-TUDASR: Computationally efficient multi-modality transformer based unsupervised domain adaptive super-resolution approach [0.721481968833929]
  This paper proposes an unsupervised Transformer-based framework for WCE image enhancement without paired low-resolution (LR) and high-resolution (HR) training data.<n>A domain-adaptive degradation network synthesizes realistic WCE-like LR images from HR conventional endoscopy images, reducing the domain gap.<n>No-reference quality metrics, including BRISQUE, PIQE, NIQE, and the domain-specific EndoQM, show that CEM-TUDASR consistently outperforms existing unsupervised SR methods.
  arXiv  Detail & Related papers  (2026-09-10T08:10:00Z)
• SCR-Guided Difficulty-Aware Optimization for Infrared Small Target Detection [0.0]
  Infrared small target detection remains challenging due to severe background clutter, low contrast, and weak spatial responses.<n>We propose REEM (Reweighted Explicit-visibility Enhanced Modulation), a lightweight SCR-guided difficulty-aware optimization framework.
  arXiv  Detail & Related papers  (2026-06-17T07:53:56Z)
• Spectral Dynamic Attention Network for Hyperspectral Image Super-Resolution [45.72534786125302]
  Spectral Dynamic Attention Network (SDANet) is a framework designed to adaptively suppress redundant spectral interactions.<n>It achieves state-of-the-art HISR performance while maintaining competitive efficiency.
  arXiv  Detail & Related papers  (2026-04-30T02:13:33Z)
• Compressive sensing inspired self-supervised single-pixel imaging [48.79613794195208]
  SISTA-Net is a compressive sensing-inspired self-supervised method for single-pixel imaging.<n>We leverage deep nonlinear networks as adaptive sparse transforms combined with a learnable soft-thresholding operator to impose explicit physical sparsity in the latent domain.<n>Experiments on multiple simulation scenarios demonstrate that SISTA-Net outperforms state-of-the-art methods by 2.6 dB in PSNR.
  arXiv  Detail & Related papers  (2026-03-31T13:31:43Z)
• Amped: Adaptive Multi-stage Non-edge Pruning for Edge Detection [53.59823657644554]
  We propose an Adaptive Multi-stage non-edge Pruning framework for Edge Detection(Amped)<n>Amped identifies high-confidence non-edge tokens and removes them as early as possible to substantially reduce computation.<n>To mitigate the structural complexity of existing edge detection networks, we introduce a simple yet high-performance Transformer-based model, termed Streamline Edge Detector(SED)
  arXiv  Detail & Related papers  (2026-03-29T12:26:35Z)
• When Small Variations Become Big Failures: Reliability Challenges in Compute-in-Memory Neural Accelerators [10.520875052654771]
  We show that even small device variations can lead to disproportionately high accuracy degradation and catastrophic failures in safety-critical inference workloads.<n>We introduce SWIM, a selective write-verify mechanism that strategically applies verification only where it is most impactful.<n>Finally, we explore a learning-centric solution that improves realistic worst-case performance by training networks with right-censored noise.
  arXiv  Detail & Related papers  (2026-03-03T20:08:25Z)
• Enhanced Evolutionary Multi-Objective Deep Reinforcement Learning for Reliable and Efficient Wireless Rechargeable Sensor Networks [44.91945854166524]
  Wireless rechargeable sensor networks (WRSNs) with mobile charging capabilities offer a promising solution to extend network lifetime.<n>WRSNs face critical challenges from the inherent trade-off between maximizing the node survival rates and maximizing charging energy efficiency.
  arXiv  Detail & Related papers  (2025-10-24T03:30:00Z)
• YOLO11-CR: a Lightweight Convolution-and-Attention Framework for Accurate Fatigue Driving Detection [0.0]
  This paper introduces YOLO11-CR, a lightweight and efficient object detection model tailored for real-time fatigue monitoring.<n>YOLO11-CR introduces two key modules: the Convolution-and-Attention Fusion Module (CAFM) and the Rectangular Module (RCM)<n>Experiments on the DSM dataset demonstrated that YOLO11-CR achieves a precision of 87.17%, recall of 83.86%, mAP@50 of 88.09%, and mAP@50-95 of 55.93%.
  arXiv  Detail & Related papers  (2025-08-16T07:19:04Z)
• Stop-band Energy Constraint for Orthogonal Tunable Wavelet Units in Convolutional Neural Networks for Computer Vision problems [8.116961165681603]
  This work introduces a stop-band energy constraint for filters in tunable wavelet units with a lattice structure, aimed at improving image classification and anomaly detection in CNNs.<n> Integrated into ResNet-18, the method enhances convolution, pooling, and downsampling operations, yielding accuracy gains of 2.48% on CIFAR-10 and 13.56% on the Describable Textures dataset.<n>On the MVTec hazelnut anomaly detection task, the proposed method achieves competitive results in both segmentation and detection, outperforming existing approaches.
  arXiv  Detail & Related papers  (2025-07-21T23:57:03Z)
• Backscattering-Based Security in Wireless Power Transfer Applied to Battery-Free BLE Sensors [44.99833362998488]
  Integration of security and energy efficiency in Internet of Things systems remains a critical challenge.<n>This paper explores the scalability and protocol-agnostic nature of a backscattering-based security mechanism by integrating it into Bluetooth Low Energy battery-free Wireless Sensor Network.
  arXiv  Detail & Related papers  (2025-07-17T12:15:09Z)
• Spectral Feature Extraction for Robust Network Intrusion Detection Using MFCCs [0.4779196219827506]
  We propose a novel approach for identifying anomalies in IoT network traffic by leveraging the Mel-frequency cepstral coefficients (MFCC) and ResNet-18.<n>Our approach combines the strengths of MFCCs with the robust feature extraction capabilities of ResNet-18, offering a powerful framework for anomaly detection.
  arXiv  Detail & Related papers  (2025-07-14T01:25:26Z)
• Deep Learning Framework for Infrastructure Maintenance: Crack Detection and High-Resolution Imaging of Infrastructure Surfaces [0.7025445595542579]
  This study developed framework consisting of convolutional neural network (CNN) and efficient sub-pixel convolutional neural network (ESPCNN)<n> ESPCNN, which is the lightweight super-resolution technique, generated high-resolution infrastructure image of positive distress obtained from CNN.<n>The proposed framework is expected to help the highway agencies in accurately performing distress detection and assist in efficient asset management practices.
  arXiv  Detail & Related papers  (2025-05-06T20:52:58Z)
• Advanced YOLO-based Real-time Power Line Detection for Vegetation Management [0.0]
  This paper introduces an intelligent real-time monitoring framework for detecting power lines and adjacent vegetation.<n>It is developed based on the deep-learning Convolutional Neural Network (CNN), You Only Look Once (YOLO)
  arXiv  Detail & Related papers  (2025-02-26T01:21:06Z)
• Towards Stabilized and Efficient Diffusion Transformers through Long-Skip-Connections with Spectral Constraints [51.83081671798784]
  Diffusion Transformers (DiT) have emerged as a powerful architecture for image and video generation, offering superior quality and scalability.<n>DiT's practical application suffers from inherent dynamic feature instability, leading to error amplification during cached inference.<n>We propose Skip-DiT, a novel DiT variant enhanced with Long-Skip-Connections (LSCs) - the key efficiency component in U-Nets.
  arXiv  Detail & Related papers  (2024-11-26T17:28:10Z)
• Convolutional Neural Network Design and Evaluation for Real-Time Multivariate Time Series Fault Detection in Spacecraft Attitude Sensors [41.94295877935867]
  This paper presents a novel approach to detecting stuck values within the Accelerometer and Inertial Measurement Unit of a drone-like spacecraft. A multi-channel Convolutional Neural Network (CNN) is used to perform multi-target classification and independently detect faults in the sensors. An integration methodology is proposed to enable the network to effectively detect anomalies and trigger recovery actions at the system level.
  arXiv  Detail & Related papers  (2024-10-11T09:36:38Z)
• Multiagent Reinforcement Learning with an Attention Mechanism for Improving Energy Efficiency in LoRa Networks [52.96907334080273]
  As the network scale increases, the energy efficiency of LoRa networks decreases sharply due to severe packet collisions. We propose a transmission parameter allocation algorithm based on multiagent reinforcement learning (MALoRa) Simulation results demonstrate that MALoRa significantly improves the system EE compared with baseline algorithms.
  arXiv  Detail & Related papers  (2023-09-16T11:37:23Z)
• ESSAformer: Efficient Transformer for Hyperspectral Image Super-resolution [76.7408734079706]
  Single hyperspectral image super-resolution (single-HSI-SR) aims to restore a high-resolution hyperspectral image from a low-resolution observation. We propose ESSAformer, an ESSA attention-embedded Transformer network for single-HSI-SR with an iterative refining structure.
  arXiv  Detail & Related papers  (2023-07-26T07:45:14Z)
• FemtoDet: An Object Detection Baseline for Energy Versus Performance Tradeoffs [27.006082622843653]
  Vision applications of convolutional neural networks, such as always-on surveillance cameras, are critical for energy constraints. This paper aims to serve as a baseline by designing detectors to reach tradeoffs between energy and performance from two perspectives.
  arXiv  Detail & Related papers  (2023-01-17T06:24:08Z)
• Asymmetric CNN for image super-resolution [102.96131810686231]
  Deep convolutional neural networks (CNNs) have been widely applied for low-level vision over the past five years. We propose an asymmetric CNN (ACNet) comprising an asymmetric block (AB), a mem?ory enhancement block (MEB) and a high-frequency feature enhancement block (HFFEB) for image super-resolution. Our ACNet can effectively address single image super-resolution (SISR), blind SISR and blind SISR of blind noise problems.
  arXiv  Detail & Related papers  (2021-03-25T07:10:46Z)
• Learning Frequency-aware Dynamic Network for Efficient Super-Resolution [56.98668484450857]
  This paper explores a novel frequency-aware dynamic network for dividing the input into multiple parts according to its coefficients in the discrete cosine transform (DCT) domain. In practice, the high-frequency part will be processed using expensive operations and the lower-frequency part is assigned with cheap operations to relieve the computation burden. Experiments conducted on benchmark SISR models and datasets show that the frequency-aware dynamic network can be employed for various SISR neural architectures.
  arXiv  Detail & Related papers  (2021-03-15T12:54:26Z)
• Anchor-free Small-scale Multispectral Pedestrian Detection [88.7497134369344]
  We propose a method for effective and efficient multispectral fusion of the two modalities in an adapted single-stage anchor-free base architecture. We aim at learning pedestrian representations based on object center and scale rather than direct bounding box predictions. Results show our method's effectiveness in detecting small-scaled pedestrians.
  arXiv  Detail & Related papers  (2020-08-19T13:13:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.