ABCDWaveNet: Advancing Robust Road Ponding Detection in Fog through Dynamic Frequency-Spatial Synergy

• URL: http://arxiv.org/abs/2504.05112v1
• Date: Mon, 07 Apr 2025 14:15:48 GMT
• Title: ABCDWaveNet: Advancing Robust Road Ponding Detection in Fog through Dynamic Frequency-Spatial Synergy
• Authors: Ronghui Zhang, Dakang Lyu, Tengfei Li, Yunfan Wu, Ujjal Manandhar, Benfei Wang, Junzhou Chen, Bolin Gao, Danwei Wang, Yiqiu Tan,
• Abstract summary: Road ponding presents a significant threat to vehicle safety, particularly in adverse fog conditions.<n>We propose ABCDWaveNet, a novel deep learning framework leveraging Dynamic Frequency-Spatial Synergy for robust ponding detection in fog.
• Score: 11.434939222396569
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Road ponding presents a significant threat to vehicle safety, particularly in adverse fog conditions, where reliable detection remains a persistent challenge for Advanced Driver Assistance Systems (ADAS). To address this, we propose ABCDWaveNet, a novel deep learning framework leveraging Dynamic Frequency-Spatial Synergy for robust ponding detection in fog. The core of ABCDWaveNet achieves this synergy by integrating dynamic convolution for adaptive feature extraction across varying visibilities with a wavelet-based module for synergistic frequency-spatial feature enhancement, significantly improving robustness against fog interference. Building on this foundation, ABCDWaveNet captures multi-scale structural and contextual information, subsequently employing an Adaptive Attention Coupling Gate (AACG) to adaptively fuse global and local features for enhanced accuracy. To facilitate realistic evaluations under combined adverse conditions, we introduce the Foggy Low-Light Puddle dataset. Extensive experiments demonstrate that ABCDWaveNet establishes new state-of-the-art performance, achieving significant Intersection over Union (IoU) gains of 3.51%, 1.75%, and 1.03% on the Foggy-Puddle, Puddle-1000, and our Foggy Low-Light Puddle datasets, respectively. Furthermore, its processing speed of 25.48 FPS on an NVIDIA Jetson AGX Orin confirms its suitability for ADAS deployment. These findings underscore the effectiveness of the proposed Dynamic Frequency-Spatial Synergy within ABCDWaveNet, offering valuable insights for developing proactive road safety solutions capable of operating reliably in challenging weather conditions.

Related papers

• FUSE: Label-Free Image-Event Joint Monocular Depth Estimation via Frequency-Decoupled Alignment and Degradation-Robust Fusion [63.87313550399871]
  Image-event joint depth estimation methods leverage complementary modalities for robust perception, yet face challenges in generalizability.<n>We propose Self-supervised Transfer (PST) and FrequencyDe-coupled Fusion module (FreDF)<n>PST establishes cross-modal knowledge transfer through latent space alignment with image foundation models.<n>FreDF explicitly decouples high-frequency edge features from low-frequency structural components, resolving modality-specific frequency mismatches.
  arXiv  Detail & Related papers  (2025-03-25T15:04:53Z)
• RobuRCDet: Enhancing Robustness of Radar-Camera Fusion in Bird's Eye View for 3D Object Detection [68.99784784185019]
  Poor lighting or adverse weather conditions degrade camera performance. Radar suffers from noise and positional ambiguity. We propose RobuRCDet, a robust object detection model in BEV.
  arXiv  Detail & Related papers  (2025-02-18T17:17:38Z)
• AGSENet: A Robust Road Ponding Detection Method for Proactive Traffic Safety [30.305692955291033]
  Road ponding poses a serious threat to road safety by causing vehicles to lose control and leading to accidents ranging from minor fender benders to severe collisions. Existing technologies struggle to accurately identify road ponding due to complex road textures and variable ponding coloration influenced by reflection characteristics. We propose a novel approach called Self-Attention-based Global Saliency-Enhanced Network (AGSENet) for proactive road ponding detection and traffic safety improvement.
  arXiv  Detail & Related papers  (2024-10-22T13:21:36Z)
• OFDM-Standard Compatible SC-NOFS Waveforms for Low-Latency and Jitter-Tolerance Industrial IoT Communications [53.398544571833135]
  This work proposes a spectrally efficient irregular Sinc (irSinc) shaping technique, revisiting the traditional Sinc back to 1924. irSinc yields a signal with increased spectral efficiency without sacrificing error performance. Our signal achieves faster data transmission within the same spectral bandwidth through 5G standard signal configuration.
  arXiv  Detail & Related papers  (2024-06-07T09:20:30Z)
• Cooperative Students: Navigating Unsupervised Domain Adaptation in Nighttime Object Detection [1.6624384368855527]
  Unsupervised Domain Adaptation (UDA) has shown significant advancements in object detection under well-lit conditions. UDA's performance degrades notably in low-visibility scenarios, especially at night. To address this problem, we propose a textbfCooperative textbfStudents (textbfCoS) framework.
  arXiv  Detail & Related papers  (2024-04-02T14:26:18Z)
• AMSP-UOD: When Vortex Convolution and Stochastic Perturbation Meet Underwater Object Detection [40.532331552038485]
  We present a novel Amplitude-Modulated Perturbation and Vortex Convolutional Network, AMSP-UOD. AMSP-UOD addresses the impact of non-ideal imaging factors on detection accuracy in complex underwater environments. Our method outperforms existing state-of-the-art methods in terms of accuracy and noise immunity.
  arXiv  Detail & Related papers  (2023-08-23T05:03:45Z)
• Enhancing Reliability in Federated mmWave Networks: A Practical and Scalable Solution using Radar-Aided Dynamic Blockage Recognition [14.18507067281377]
  This article introduces a new method to improve the dependability of millimeter-wave (mmWave) and terahertz (THz) network services in dynamic outdoor environments. In these settings, line-of-sight (LoS) connections are easily interrupted by moving obstacles like humans and vehicles. The proposed approach, coined as Radar-aided blockage Dynamic Recognition (RaDaR), leverages radar measurements and federated learning (FL) to train a dual-output neural network (NN) model.
  arXiv  Detail & Related papers  (2023-06-22T10:10:25Z)
• Spectrum Breathing: Protecting Over-the-Air Federated Learning Against Interference [73.63024765499719]
  Mobile networks can be compromised by interference from neighboring cells or jammers. We propose Spectrum Breathing, which cascades-gradient pruning and spread spectrum to suppress interference without bandwidth expansion. We show a performance tradeoff between gradient-pruning and interference-induced error as regulated by the breathing depth.
  arXiv  Detail & Related papers  (2023-05-10T07:05:43Z)
• DADFNet: Dual Attention and Dual Frequency-Guided Dehazing Network for Video-Empowered Intelligent Transportation [79.18450119567315]
  Adverse weather conditions pose severe challenges for video-based transportation surveillance. We propose a dual attention and dual frequency-guided dehazing network (termed DADFNet) for real-time visibility enhancement.
  arXiv  Detail & Related papers  (2023-04-19T11:55:30Z)
• GraSens: A Gabor Residual Anti-aliasing Sensing Framework for Action Recognition using WiFi [52.530330427538885]
  WiFi-based human action recognition (HAR) has been regarded as a promising solution in applications such as smart living and remote monitoring. We propose an end-to-end Gabor residual anti-aliasing sensing network (GraSens) to directly recognize the actions using the WiFi signals from the wireless devices in diverse scenarios.
  arXiv  Detail & Related papers  (2022-05-24T10:20:16Z)
• Deep Reinforcement Learning Control for Radar Detection and Tracking in Congested Spectral Environments [8.103366584285645]
  A radar learns to vary the bandwidth and center frequency of its linear frequency modulated (LFM) waveforms to mitigate mutual interference with other systems. We extend the DQL-based approach to incorporate Double Q-learning and a recurrent neural network to form a Double Deep Recurrent Q-Network. Our experimental results indicate that the proposed Deep RL approach significantly improves radar detection performance in congested spectral environments.
  arXiv  Detail & Related papers  (2020-06-23T17:21:28Z)

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.