2022
DOI: 10.1186/s13634-022-00931-x
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YOLO-LRDD: a lightweight method for road damage detection based on improved YOLOv5s

Abstract: In computer vision, timely and accurate execution of object identification tasks is critical. However, present road damage detection approaches based on deep learning suffer from complex models and computationally time-consuming issues. To address these issues, we present a lightweight model for road damage identification by enhancing the YOLOv5s approach. The resulting algorithm, YOLO-LRDD, provides a good balance of detection precision and speed. First, we propose the novel backbone network Shuffle-ECANet by… Show more

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Cited by 55 publications
(18 citation statements)
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References 34 publications
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“…CA module refers to Channel Attention module, which is used to enhance the feature extraction capability of convolutional neural networks. The structure diagram of CA module is shown in the figure [3] . First, the input feature graph is globally average pooled in the width and height directions to obtain the feature graph in the width and height directions.…”
Section: Ca Attention Mechanism Modulementioning
confidence: 99%
“…CA module refers to Channel Attention module, which is used to enhance the feature extraction capability of convolutional neural networks. The structure diagram of CA module is shown in the figure [3] . First, the input feature graph is globally average pooled in the width and height directions to obtain the feature graph in the width and height directions.…”
Section: Ca Attention Mechanism Modulementioning
confidence: 99%
“…Another recent work is Wan et al (2022). In it, a lightweight DL you only look once -lightweight method for road damage detection (YOLO-LRDD) is used and compared to Faster R-CNN.…”
Section: Related Workmentioning
confidence: 99%
“…In 2020, the researchers employed the U-Net architecture and the YOLOv3 algorithm to detect and classify various types of road damages in China [43]. Using a new backbone, Wan et al [44] presented the YOLO-LRDD algorithm. They increased the quantity of data in the RDD2020 dataset by using the data collected in China.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Using a new backbone, Wan et al. [44] presented the YOLO‐LRDD algorithm. They increased the quantity of data in the RDD2020 dataset by using the data collected in China.…”
Section: Literature Reviewmentioning
confidence: 99%