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Depth Perception and Spatial Vision01:15

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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Related Experiment Video

Updated: Jan 13, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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The TDGL Module: A Fast Multi-Scale Vision Sensor Based on a Transformation Dilated Grouped Layer.

Leilei Xie1, Fenghua Zhu2, Zhixue Wang2

  • 1School of Rail Transportation, Shandong Jiaotong University, Jinan 250357, China.

Sensors (Basel, Switzerland)
|January 7, 2026
PubMed
Summary

This study introduces the Transformation Dilated Grouped Layer (TDGL) module for faster and more accurate road object detection. The TDGL enhances deep learning models, improving multi-scale feature capture for autonomous vehicles.

Keywords:
data normalizationexpansion rateobject detectionspatial pyramid pooling

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Area of Science:

  • Computer Vision
  • Deep Learning
  • Autonomous Systems

Background:

  • Capturing multi-scale object features is vital for road object detection.
  • Traditional methods struggle with dynamic adaptation and full utilization of spatial hierarchies.

Purpose of the Study:

  • To propose a novel Transformation Dilated Grouped Layer (TDGL) module for efficient and accurate road target feature extraction.
  • To enhance deep learning-based vision sensors for autonomous driving applications.

Main Methods:

  • Developed the Transformation Dilated Grouped Layer (TDGL) module based on Global Layer Normalization Convolution (GLConv).
  • Incorporated scaling, offset parameters, modified dilation strategies, and grouped convolution within the GLConv unit.
  • Integrated the TDGL module into YOLO models to create the TDGL Net feature extractor.

Main Results:

  • The TDGL Net achieved a mean Average Precision (mAP) of 40.3% on the BDD100K dataset with approximately 3.1 million parameters.
  • Demonstrated an inference speed of 58 FPS after transformation optimization, meeting real-time detection requirements.

Conclusions:

  • The TDGL module effectively captures multi-scale features, optimizes spatial information processing, and reduces computational costs.
  • The TDGL Net offers a promising solution for real-time road object detection in autonomous vehicles.