Real-Time Lane Detection via Efficient Feature Alignment and Covariance Optimization for Low-Power Embedded Systems

• URL: http://arxiv.org/abs/2601.01696v1
• Date: Mon, 05 Jan 2026 00:06:06 GMT
• Title: Real-Time Lane Detection via Efficient Feature Alignment and Covariance Optimization for Low-Power Embedded Systems
• Authors: Yian Liu, Xiong Wang, Ping Xu, Lei Zhu, Ming Yan, Linyun Xue,
• Abstract summary: Real-time lane detection in embedded systems faces significant challenges due to subtle and sparse visual signals in RGB images.<n>We propose an innovative Covariance Distribution Optimization (CDO) module specifically designed for efficient, real-time applications.<n>CDO module aligns lane feature distributions closely with ground-truth labels, significantly enhancing detection accuracy without increasing computational complexity.
• Score: 22.603468261037975
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-time lane detection in embedded systems encounters significant challenges due to subtle and sparse visual signals in RGB images, often constrained by limited computational resources and power consumption. Although deep learning models for lane detection categorized into segmentation-based, anchor-based, and curve-based methods there remains a scarcity of universally applicable optimization techniques tailored for low-power embedded environments. To overcome this, we propose an innovative Covariance Distribution Optimization (CDO) module specifically designed for efficient, real-time applications. The CDO module aligns lane feature distributions closely with ground-truth labels, significantly enhancing detection accuracy without increasing computational complexity. Evaluations were conducted on six diverse models across all three method categories, including two optimized for real-time applications and four state-of-the-art (SOTA) models, tested comprehensively on three major datasets: CULane, TuSimple, and LLAMAS. Experimental results demonstrate accuracy improvements ranging from 0.01% to 1.5%. The proposed CDO module is characterized by ease of integration into existing systems without structural modifications and utilizes existing model parameters to facilitate ongoing training, thus offering substantial benefits in performance, power efficiency, and operational flexibility in embedded systems.

Related papers

• Real-Time LiDAR Super-Resolution via Frequency-Aware Multi-Scale Fusion [0.4078247440919472]
  FLASH (Frequency-aware LiDAR Adaptive Super-resolution with Hierarchical fusion) is a novel framework that overcomes limitations through dual-domain processing.<n> FLASH integrates two key innovations: (i) Frequency-Aware Window Attention that combines local spatial attention with global frequency-domain analysis via FFT, capturing both fine-grained geometry and periodic scanning patterns at log-linear complexity, and (ii) Adaptive Multi-Scale Fusion that replaces conventional skip connections with learned position-specific feature aggregation, enhanced by CBAM attention for dynamic feature selection.
  arXiv  Detail & Related papers  (2025-11-10T18:38:15Z)
• Source-Free Object Detection with Detection Transformer [59.33653163035064]
  Source-Free Object Detection (SFOD) enables knowledge transfer from a source domain to an unsupervised target domain for object detection without access to source data.<n>Most existing SFOD approaches are either confined to conventional object detection (OD) models like Faster R-CNN or designed as general solutions without tailored adaptations for novel OD architectures, especially Detection Transformer (DETR)<n>In this paper, we introduce Feature Reweighting ANd Contrastive Learning NetworK (FRANCK), a novel SFOD framework specifically designed to perform query-centric feature enhancement for DETRs.
  arXiv  Detail & Related papers  (2025-10-13T07:35:04Z)
• POLARON: Precision-aware On-device Learning and Adaptive Runtime-cONfigurable AI acceleration [0.0]
  This work presents a SIMD-enabled, multi-precision MAC engine that performs efficient multiply-accumulate operations.<n>The architecture incorporates a layer adaptive precision strategy to align computational accuracy with workload sensitivity.<n>Results demonstrate up to 2x improvement in PDP and 3x reduction in resource usage compared to SoTA designs.
  arXiv  Detail & Related papers  (2025-06-10T13:33:02Z)
• R-Sparse: Rank-Aware Activation Sparsity for Efficient LLM Inference [77.47238561728459]
  R-Sparse is a training-free activation sparsity approach capable of achieving high sparsity levels in advanced LLMs.<n> Experiments on Llama-2/3 and Mistral models across ten diverse tasks demonstrate that R-Sparse achieves comparable performance at 50% model-level sparsity.
  arXiv  Detail & Related papers  (2025-04-28T03:30:32Z)
• Sample-efficient diffusion-based control of complex nonlinear systems [12.75120974078924]
  SEDC is a novel diffusion-based control framework addressing high-dimensional state-action spaces, nonlinear system dynamics, and the gap between non-optimal training data and near-optimal control solutions.<n>Our approach represents a significant advancement in sample-efficient control of complex nonlinear systems.
  arXiv  Detail & Related papers  (2025-02-25T06:30:04Z)
• Lightweight Deep Learning Framework for Accurate Particle Flow Energy Reconstruction [8.598010350935596]
  This paper systematically evaluates a deep learning reconstruction framework.<n>We design a hybrid loss function combining weighted mean squared with error structural similarity index.<n>We enhance the model's capability to capture cross-modaltemporal correlations and energy-displacement nonlinearities.
  arXiv  Detail & Related papers  (2024-10-08T11:49:18Z)
• Edge-Efficient Deep Learning Models for Automatic Modulation Classification: A Performance Analysis [0.7428236410246183]
  We investigate optimized convolutional neural networks (CNNs) developed for automatic modulation classification (AMC) of wireless signals. We propose optimized models with the combinations of these techniques to fuse the complementary optimization benefits. The experimental results show that the proposed individual and combined optimization techniques are highly effective for developing models with significantly less complexity.
  arXiv  Detail & Related papers  (2024-04-11T06:08:23Z)
• Parameter-Adaptive Approximate MPC: Tuning Neural-Network Controllers without Retraining [50.00291020618743]
  This work introduces a novel, parameter-adaptive AMPC architecture capable of online tuning without recomputing large datasets and retraining. We showcase the effectiveness of parameter-adaptive AMPC by controlling the swing-ups of two different real cartpole systems with a severely resource-constrained microcontroller (MCU) Taken together, these contributions represent a marked step toward the practical application of AMPC in real-world systems.
  arXiv  Detail & Related papers  (2024-04-08T20:02:19Z)
• When Parameter-efficient Tuning Meets General-purpose Vision-language Models [65.19127815275307]
  PETAL revolutionizes the training process by requiring only 0.5% of the total parameters, achieved through a unique mode approximation technique. Our experiments reveal that PETAL not only outperforms current state-of-the-art methods in most scenarios but also surpasses full fine-tuning models in effectiveness.
  arXiv  Detail & Related papers  (2023-12-16T17:13:08Z)
• Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inference [47.460898983429374]
  We introduce an ensemble Kalman filter (EnKF) into the non-mean-field (NMF) variational inference framework to approximate the posterior distribution of the latent states. This novel marriage between EnKF and GPSSM not only eliminates the need for extensive parameterization in learning variational distributions, but also enables an interpretable, closed-form approximation of the evidence lower bound (ELBO) We demonstrate that the resulting EnKF-aided online algorithm embodies a principled objective function by ensuring data-fitting accuracy while incorporating model regularizations to mitigate overfitting.
  arXiv  Detail & Related papers  (2023-12-10T15:22:30Z)
• PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detection [65.24854366973794]
  Node-level graph anomaly detection (GAD) plays a critical role in identifying anomalous nodes from graph-structured data in domains such as medicine, social networks, and e-commerce. We introduce a simple method termed PREprocessing and Matching (PREM for short) to improve the efficiency of GAD. Our approach streamlines GAD, reducing time and memory consumption while maintaining powerful anomaly detection capabilities.
  arXiv  Detail & Related papers  (2023-10-18T02:59:57Z)
• Optimization-driven Machine Learning for Intelligent Reflecting Surfaces Assisted Wireless Networks [82.33619654835348]
  Intelligent surface (IRS) has been employed to reshape the wireless channels by controlling individual scattering elements' phase shifts. Due to the large size of scattering elements, the passive beamforming is typically challenged by the high computational complexity. In this article, we focus on machine learning (ML) approaches for performance in IRS-assisted wireless networks.
  arXiv  Detail & Related papers  (2020-08-29T08:39:43Z)

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.