Version 1
: Received: 28 May 2023 / Approved: 31 May 2023 / Online: 31 May 2023 (07:25:27 CEST)
How to cite:
Ogunrinde, I.O.; Bernadin, S. Deep Camera-Radar Fusion with Attention Framework for Autonomous Vehicle Vision in Foggy Weather Conditions. Preprints2023, 2023052180. https://doi.org/10.20944/preprints202305.2180.v1
Ogunrinde, I.O.; Bernadin, S. Deep Camera-Radar Fusion with Attention Framework for Autonomous Vehicle Vision in Foggy Weather Conditions. Preprints 2023, 2023052180. https://doi.org/10.20944/preprints202305.2180.v1
Ogunrinde, I.O.; Bernadin, S. Deep Camera-Radar Fusion with Attention Framework for Autonomous Vehicle Vision in Foggy Weather Conditions. Preprints2023, 2023052180. https://doi.org/10.20944/preprints202305.2180.v1
APA Style
Ogunrinde, I.O., & Bernadin, S. (2023). Deep Camera-Radar Fusion with Attention Framework for Autonomous Vehicle Vision in Foggy Weather Conditions. Preprints. https://doi.org/10.20944/preprints202305.2180.v1
Chicago/Turabian Style
Ogunrinde, I.O. and Shonda Bernadin. 2023 "Deep Camera-Radar Fusion with Attention Framework for Autonomous Vehicle Vision in Foggy Weather Conditions" Preprints. https://doi.org/10.20944/preprints202305.2180.v1
Abstract
AVs suffer reduced maneuverability and performance due to the degradation in sensor performances in fog. Such degradation causes significant object detection errors essential for AVs' safety-critical conditions. For instance, YOLOv5 performs significantly well under favorable weather but suffers miss detections and false positives due to atmospheric scattering caused by fog particles. Existing deep object detection techniques often exhibit a high degree of accuracy. The drawback is being sluggish at object detection in fog. Object detection methods with fast detection speed have been obtained using deep learning at the expense of accuracy. The problem of the lack of balance between detection speed and accuracy in fog persist. This paper presents an improved YOLOv5-based multi-sensor fusion network that combines radar's object detection with a camera image bounding box. We transformed radar detection by mapping the radar detections into a two-dimensional image coordinate and projected the resultant radar image on the camera image. Using the attention mechanism, we emphasized and improved important feature representation used for object detection while reducing high-level feature information loss. We trained and tested our multi-sensor fusion network on clear and multi-fog weather datasets obtained from the CARLA simulator. Our result shows that the proposed method significantly enhances the detection of distant and small objects. Our small CR-YOLOnet model best strikes a balance between accuracy and speed with an accuracy of 0.849 at 69 fps.
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
