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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
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Updated: May 21, 2025

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CFI-Former: Efficient lane detection by multi-granularity perceptual query attention transformer.

Rong Gao1, Siqi Hu2, Lingyu Yan2

  • 1School of Computer Science, Hubei University of Technology, Wuhan, 430068, China; State Key Laboratory for Novel Software Technology at Nanjing University, Nanjing, 210023, China.

Neural Networks : the Official Journal of the International Neural Network Society
|March 18, 2025
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Summary

CFI-Former improves lane detection by using multi-granularity perceptual query attention to refine feature details and reduce redundant information. A novel weighted adaptive loss enhances performance in challenging scenarios.

Keywords:
Advanced driver assistance systemLane detectionMulti-granularityTransformer

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Area of Science:

  • Computer Vision
  • Deep Learning
  • Artificial Intelligence

Background:

  • Transformer methods have advanced lane detection performance.
  • Existing methods suffer from repetitive and invalid information in query sequences, biasing localized feature processing.
  • Inaccurate lane line shape constraints hinder precise detection.

Purpose of the Study:

  • To propose CFI-Former, a novel transformer-based lane detection method.
  • To enhance lane detection accuracy by addressing redundant information and improving feature detail extraction.
  • To improve robustness in challenging lane detection scenarios.

Main Methods:

  • Introduced a multi-granularity perceptual query attention (GQA) module for extracting detailed lane information.
  • Implemented a two-stage query process (coarse-to-fine) to filter irrelevant information.
  • Developed a weighted adaptive Intersection over Union (IoU) loss (Lφ-IoU) to improve performance on difficult cases.

Main Results:

  • The GQA module effectively extracts multi-granularity lane features from global to local.
  • The two-stage query efficiently filters redundant data, focusing attention on relevant regions.
  • The weighted adaptive IoU loss adaptively adjusts gradients for high IoU objects, enhancing challenging scenario performance.

Conclusions:

  • CFI-Former achieves more accurate lane detection compared to baseline methods.
  • The proposed GQA module and weighted adaptive IoU loss contribute to improved lane detection capabilities.
  • CFI-Former demonstrates superior performance on benchmark lane detection datasets.