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Peer-reviewedCC BY 4.0PLOS One

Deep learning-based detection of construction waste in complex scenarios with an improved lightweight algorithm

  • YiZhong Yang1
  • YeXue Li2
  • ShengWei Li3
  • MaoHu Tao4
  • Xuzhi Chen5
5 affiliations
  1. 1School of Automotive and Traffic Engineering, Hubei University of Arts and Science, Xiangyang, China
  2. 2School of Civil Engineering and Architecture, Hubei University of Arts and Science, Xiangyang, China
  3. 3Hubei Key Laboratory of Vehicle-Infrastructure Collaboration and Traffic Control, Xiangyang, China
  4. 4School of Mathematics and Statistics, Hubei University of Arts and Science, Xiangyang, China
  5. 5Hubei Gongjian Hanjiang Engineering Co., Ltd., Xiangyang, China
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Abstract

To tackle the challenges in construction waste detection under complex scenarios—such as insufficient recognition accuracy, significant feature variations among identical waste categories, limited publicly available CDW datasets, and excessive computational resource consumption that hinders real-time performance—this paper proposes a novel improved algorithm, GTS-YOLO. Built on YOLOv11, GTS-YOLO achieves model lightweighting by optimizing the C3K2 module in the backbone network, enhances detection accuracy effectively through integrating spatial attention mechanisms between the backbone and neck, and redesigns the detection head with reference to the task alignment principle to better handle object detection of construction waste under occlusion and deformation. On our self-constructed 10-category dataset, compared with YOLOv11n, GTS-YOLO increases mAP50 by 3.24% to 67.47%, improves Precision by 3.1%, and reduces the parameter count by 19.4%, In addition, the model size is only 4.23 MB, which is approximately 20.6% smaller than YOLOv11n, demonstrating the effectiveness of the proposed lightweight design.achieving an effective balance between accuracy and efficiency.We have also validated the model performance on public datasets, demonstrating its generalization ability across diverse scenarios.

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Cite

BibTeXRIS
BibTeX
@article{yang2026deep,
  title = {{Deep learning-based detection of construction waste in complex scenarios with an improved lightweight algorithm}},
  author = {YiZhong Yang and YeXue Li and ShengWei Li and MaoHu Tao and Xuzhi Chen},
  journal = {PLOS One},
  year = {2026},
  month = sep,
  doi = {10.1371/journal.pone.0356914},
  url = {https://doi.org/10.1371/journal.pone.0356914},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Deep learning-based detection of construction waste in complex scenarios with an improved lightweight algorithm
AU  - YiZhong Yang
AU  - YeXue Li
AU  - ShengWei Li
AU  - MaoHu Tao
AU  - Xuzhi Chen
PY  - 2026
DA  - 2026/09/18
JO  - PLOS One
DO  - 10.1371/journal.pone.0356914
UR  - https://doi.org/10.1371/journal.pone.0356914
AB  - To tackle the challenges in construction waste detection under complex scenarios—such as insufficient recognition accuracy, significant feature variations among identical waste categories, limited publicly available CDW datasets, and excessive computational resource consumption that hinders real-time performance—this paper proposes a novel improved algorithm, GTS-YOLO. Built on YOLOv11, GTS-YOLO achieves model lightweighting by optimizing the C3K2 module in the backbone network, enhances detection accuracy effectively through integrating spatial attention mechanisms between the backbone and neck, and redesigns the detection head with reference to the task alignment principle to better handle object detection of construction waste under occlusion and deformation. On our self-constructed 10-category dataset, compared with YOLOv11n, GTS-YOLO increases mAP50 by 3.24% to 67.47%, improves Precision by 3.1%, and reduces the parameter count by 19.4%, In addition, the model size is only 4.23 MB, which is approximately 20.6% smaller than YOLOv11n, demonstrating the effectiveness of the proposed lightweight design.achieving an effective balance between accuracy and efficiency.We have also validated the model performance on public datasets, demonstrating its generalization ability across diverse scenarios.
SN  - 1932-6203
ER  -