Object Detection for Construction Waste Based on an Improved YOLOv5 Model

Qinghui Zhou (), Haoshi Liu, Yuhang Qiu and Wuchao Zheng
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Qinghui Zhou: School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Haoshi Liu: School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Yuhang Qiu: School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
Wuchao Zheng: School of Mechanical-Electronic and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China

Sustainability, 2022, vol. 15, issue 1, 1-15

Abstract: An object detection method based on an improved YOLOv5 model was proposed to enhance the accuracy of sorting construction waste. A construction waste image sample set was established by collecting construction waste images on site. These construction waste images were preprocessed using the random brightness method. A YOLOv5 object detection model was improved in terms of the convolutional block attention module (CBAM), simplified SPPF (SimSPPF) and multi-scale detection. Then, the improved YOLOv5 model was trained, validated and tested using the established construction waste image dataset and compared with other conventional models such as Faster-RCNN, YOLOv3, YOLOv4, and YOLOv7. The results show that: based on the improved YOLOv5 model, the mean average precision (mAP) on the test dataset can reach 0.9480. The overall performance of this model is better than that of other conventional models in object detection, which verifies the accuracy and availability of the proposed method.

Keywords: construction waste; computer vision; deep learning; YOLOv5; waste sorting (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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