YOLO-ARDD: An improved feature extraction and enhanced single-stage object detection network
Author: 
Yinlun XuAuthors Info & Claims
Pages 391 - 395
Abstract
Traditional road maintenance relies on expensive infrared equipment or sensors. Although target detection and recognition algorithms have been used in road detection, image processing algorithms and lightweight deep learning models often affect accuracy when detecting challenging road damage. This article introduces a target detection algorithm YOLO-ARDD that runs on cloud computing technology. The model integrates deformable convolutional modules and introduces a weighted bidirectional feature pyramid network module to enhance feature fusion. Through experiments, it is find that the detection results of YOLO-ARDD achieve an average accuracy of 94.7%, which is significantly improved by 5.94% compared to the baseline, and is 3.1% higher than the detection accuracy of the YOLOV8x advanced model. This method provides a force for road detection tasks as well as intelligent transportation and road maintenance industries.
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Index Terms
- YOLO-ARDD: An improved feature extraction and enhanced single-stage object detection network
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Published In
December 2023
467 pages
ISBN:9798400716966
DOI:10.1145/3661638
Copyright © 2023 ACM.
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Association for Computing Machinery
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Published: 01 June 2024
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AISNS 2023
AISNS 2023: 2023 International Conference on Artificial Intelligence, Systems and Network Security
December 22 - 24, 2023
Mianyang, China
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