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STC-YOLO: Small Object Detection Network for Traffic Signs in Complex Environments.

Huaqing Lai1, Liangyan Chen1, Weihua Liu1

  • 1School of Electric and Electronic Engineering, Wuhan Polytechnic University, Wuhan 430023, China.

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|June 10, 2023
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Summary

This study introduces STC-YOLO, a new network for detecting traffic signs in challenging conditions like fog and occlusion. The enhanced YOLOv5 model significantly improves detection accuracy for autonomous driving systems.

Keywords:
K-means++data augmentationloss functionmulti-scale feature fusionsmall object detection

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Autonomous Driving Systems

Background:

  • Traffic sign detection is crucial for autonomous driving safety but is hindered by adverse weather, occlusion, and lighting variations.
  • Existing methods struggle with the reliability and accuracy of traffic sign recognition in complex, real-world scenarios.
  • The need for robust datasets and specialized detection networks is evident to address these limitations.

Purpose of the Study:

  • To develop an enhanced traffic sign dataset (enhanced TT100K) with augmented challenging samples.
  • To propose a novel small traffic sign detection network (STC-YOLO) optimized for complex environments.
  • To improve the accuracy and robustness of traffic sign detection in autonomous driving applications.

Main Methods:

  • Construction of the enhanced Tsinghua-Tencent 100K (TT100K) dataset incorporating fog, snow, noise, occlusion, and blur augmentation.
  • Development of STC-YOLO, a modified YOLOv5 architecture featuring adjusted down-sampling, a small object detection layer, and a CNN-multi-head attention feature extraction module.
  • Integration of Normalized Gaussian Wasserstein distance (NWD) for improved localization accuracy of small objects and K-means++ for optimal anchor box sizing.

Main Results:

  • The STC-YOLO algorithm achieved a 9.3% higher mean average precision (mAP) compared to YOLOv5 on the enhanced TT100K dataset.
  • Experiments demonstrated superior performance in detecting small and difficult traffic signs across 45 categories.
  • STC-YOLO's performance was found to be competitive with state-of-the-art methods on public benchmarks like TT100K and CCTSDB2021.

Conclusions:

  • The proposed STC-YOLO network effectively addresses the challenges of traffic sign detection in complex environmental conditions.
  • The enhanced TT100K dataset provides a valuable resource for training and evaluating robust traffic sign detection models.
  • The integration of NWD and refined anchor box strategies significantly enhances the detection of small objects, crucial for autonomous driving safety.