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YOLO-Vehicle-Pro: A Cloud-Edge Collaborative Framework for Object Detection in Autonomous Driving under Adverse Weather Conditions

Xiguang Li, Jiafu Chen, Yunhe Sun, Na Lin, Ammar Hawbani, Liang Zhao

TL;DR

A cloud-edge collaborative object detection system, deploying models on edge devices and offloading partial computational tasks to the cloud in complex situations, and designs and implements a cloud-edge collaborative object detection system for autonomous driving.

Abstract

With the rapid advancement of autonomous driving technology, efficient and accurate object detection capabilities have become crucial factors in ensuring the safety and reliability of autonomous driving systems. However, in low-visibility environments such as hazy conditions, the performance of traditional object detection algorithms often degrades significantly, failing to meet the demands of autonomous driving. To address this challenge, this paper proposes two innovative deep learning models: YOLO-Vehicle and YOLO-Vehicle-Pro. YOLO-Vehicle is an object detection model tailored specifically for autonomous driving scenarios, employing multimodal fusion techniques to combine image and textual information for object detection. YOLO-Vehicle-Pro builds upon this foundation by introducing an improved image dehazing algorithm, enhancing detection performance in low-visibility environments. In addition to model innovation, this paper also designs and implements a cloud-edge collaborative object detection system, deploying models on edge devices and offloading partial computational tasks to the cloud in complex situations. Experimental results demonstrate that on the KITTI dataset, the YOLO-Vehicle-v1s model achieved 92.1% accuracy while maintaining a detection speed of 226 FPS and an inference time of 12ms, meeting the real-time requirements of autonomous driving. When processing hazy images, the YOLO-Vehicle-Pro model achieved a high accuracy of 82.3% mAP@50 on the Foggy Cityscapes dataset while maintaining a detection speed of 43 FPS.

YOLO-Vehicle-Pro: A Cloud-Edge Collaborative Framework for Object Detection in Autonomous Driving under Adverse Weather Conditions

TL;DR

A cloud-edge collaborative object detection system, deploying models on edge devices and offloading partial computational tasks to the cloud in complex situations, and designs and implements a cloud-edge collaborative object detection system for autonomous driving.

Abstract

With the rapid advancement of autonomous driving technology, efficient and accurate object detection capabilities have become crucial factors in ensuring the safety and reliability of autonomous driving systems. However, in low-visibility environments such as hazy conditions, the performance of traditional object detection algorithms often degrades significantly, failing to meet the demands of autonomous driving. To address this challenge, this paper proposes two innovative deep learning models: YOLO-Vehicle and YOLO-Vehicle-Pro. YOLO-Vehicle is an object detection model tailored specifically for autonomous driving scenarios, employing multimodal fusion techniques to combine image and textual information for object detection. YOLO-Vehicle-Pro builds upon this foundation by introducing an improved image dehazing algorithm, enhancing detection performance in low-visibility environments. In addition to model innovation, this paper also designs and implements a cloud-edge collaborative object detection system, deploying models on edge devices and offloading partial computational tasks to the cloud in complex situations. Experimental results demonstrate that on the KITTI dataset, the YOLO-Vehicle-v1s model achieved 92.1% accuracy while maintaining a detection speed of 226 FPS and an inference time of 12ms, meeting the real-time requirements of autonomous driving. When processing hazy images, the YOLO-Vehicle-Pro model achieved a high accuracy of 82.3% mAP@50 on the Foggy Cityscapes dataset while maintaining a detection speed of 43 FPS.

Paper Structure

This paper contains 22 sections, 16 equations, 11 figures, 5 tables.

Table of Contents

  1. INTRODUCTION
  2. RELATED WORK
  3. SYSTEM PLATFORM AND ALGORITHM
  4. System Platform Design
  5. Overall Framework
  6. YOLO-Vehicle Network Structure
  7. Text Feature Extraction
  8. Image Feature Extraction
  9. Multi-modal Feature Fusion
  10. Position Encoding and Detection Head
  11. YOLO-Vehicle-Pro Network Structure
  12. Attention of Attention-based Convolution
  13. Activation Function
  14. Loss Functions
  15. YOLO-Vehicle Loss Function
  16. ...and 7 more sections

Figures (11)

  • Figure 1: Weather condition challenges in autonomous driving scenarios. (a) Clear weather environment (Scene a) and (b) hazy environment (Scene b), where low visibility in hazy conditions leads to reduced image contrast and blurred object contours.
  • Figure 2: Edge-Cloud collaborative autonomous vehicle object detection system architecture
  • Figure 3: Structure and workflow of YOLO-Vehicle and YOLO-Vehicle-Pro models
  • Figure 4: Architecture of Attention-based Conv
  • Figure 5: Training loss values under COCO dataset
  • ...and 6 more figures