Evidence map›Paper›PMID 40603871›Full record

ArticleScientific reports2025

Hybrid-YOLOv5 for object detection of non-ferrous metals in end-of-life vehicles.

Youdong Jia, Yuhang Zhang, Rui Yang, Xinzhi Li, Zhengfang Li, Sibo Yao, Jiaxing Zeng, Ziyue Zhu, Yuanxin Zhang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

9 authors.

Youdong JiaFaculty of Mechanical and Electrical Engineering, Kunming University, Kunming, 650214, People's Republic of China.
Yuhang ZhangFaculty of Mechanical and Electrical Engineering, Kunming University, Kunming, 650214, People's Republic of China. zhangyh0918@126.com.
Rui YangFaculty of Mechanical and Electrical Engineering, Kunming University, Kunming, 650214, People's Republic of China.
Xinzhi LiFaculty of Mechanical and Electrical Engineering, Kunming University, Kunming, 650214, People's Republic of China.
Zhengfang LiFaculty of Mechanical and Electrical Engineering, Kunming University, Kunming, 650214, People's Republic of China.
Sibo YaoSchool of Mechanical Engineering & Automation, Northeastern University, Shenyang, 110819, People's Republic of China.
Jiaxing ZengFaculty of Mechanical and Electrical Engineering, Kunming University of Science and Technology, Kunming, 650500, People's Republic of China.
Ziyue ZhuFaculty of Mechanical and Electrical Engineering, Kunming University, Kunming, 650214, People's Republic of China.
Yuanxin ZhangFaculty of Mechanical and Electrical Engineering, Kunming University, Kunming, 650214, People's Republic of China.

Funding

National Natural Science Foundation of China 52205374The Special Basic Cooperative Research Programs of Yunnan Provincial Undergraduate Universities' Association 202101BA070001-157Yunnan Fundamental Research Projects 202301AT070052Yunnan Province College Students' Innovation and Entrepreneurship Project (202411393014)
6 · The paper itself

Abstract

In recent years, the detection of non-ferrous metals in end-of-life vehicles (ELVs) has become essential for improving recycling in the circular economy. Traditional methods struggle with accurate detection due to the variety of metals and challenging industrial environments. This study proposes a Hybrid-YOLOv5-based algorithm for efficiently detecting copper, aluminum, and stainless steel in ELVs. The goal is to enhance detection accuracy and computational efficiency in metal sorting. By integrating the Coarse-to-Fine (C2F) module, Squeeze-and-Excitation (SE) module, and MobileNetV3 backbone, we significantly improve performance and speed. On a dataset of 2,500 infrared images, Hybrid-YOLOv5 achieves 84.2% mAP@0.5 and 60 FPS inference speed, outperforming YOLOv3, YOLOv5, YOLOv7, and YOLOv11 by 22.2%, 12.4%, 11.1%, and 36.2% in mAP@0.5, respectively. This work provides an efficient solution for industrial metal sorting and intelligent recycling in the circular economy.

Indexed as

Deep learningEnd-of-life vehiclesInfrared imagesNon-ferrous metals detectionYOLOv5

Identifiers

PMID40603871
PMCPMC12222649

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.