Evidence map›Paper›PMID 36991979›Full record

ArticleSensors (Basel, Switzerland)2023

YOLO-LWNet: A Lightweight Road Damage Object Detection Network for Mobile Terminal Devices.

Chenguang Wu, Min Ye, Jiale Zhang, Yuchuan Ma

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Chenguang WuNational Engineering Research Center of Highway Maintenance Equipment, Chang'an University, Xi'an 710065, China.
Min YeNational Engineering Research Center of Highway Maintenance Equipment, Chang'an University, Xi'an 710065, China.ORCID 0000-0002-8301-5843
Jiale ZhangNational Engineering Research Center of Highway Maintenance Equipment, Chang'an University, Xi'an 710065, China.
Yuchuan MaNational Engineering Research Center of Highway Maintenance Equipment, Chang'an University, Xi'an 710065, China.

Funding

Innovative Talents Promotion Project of Shannxi Province of China 2020KJXX-044National Natural Science Foundation of China 51805041Science and Technology Innovation Team of Shaanxi Province 2020TD0012
6 · The paper itself

Abstract

To solve the demand for road damage object detection under the resource-constrained conditions of mobile terminal devices, in this paper, we propose the YOLO-LWNet, an efficient lightweight road damage detection algorithm for mobile terminal devices. First, a novel lightweight module, the LWC, is designed and the attention mechanism and activation function are optimized. Then, a lightweight backbone network and an efficient feature fusion network are further proposed with the LWC as the basic building units. Finally, the backbone and feature fusion network in the YOLOv5 is replaced. In this paper, two versions of the YOLO-LWNet, small and tiny, are introduced. The YOLO-LWNet was compared with the YOLOv6 and the YOLOv5 on the RDD-2020 public dataset in various performance aspects. The experimental results show that the YOLO-LWNet outperforms state-of-the-art real-time detectors in terms of balancing detection accuracy, model scale, and computational complexity in the road damage object detection task. It can better achieve the lightweight and accuracy requirements for object detection for mobile terminal devices.

Indexed as

attention mechanismlightweight networkmobile terminalobject detectionroad damage detectionYOLOv5

Identifiers

PMID36991979
PMCPMC10058657

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.