**4. Conclusions**

In construction waste sorting, inter-object occlusions and small-object detection are the two most important problems, affecting the effective performance of the construction waste detection system. In order to increase the accuracy of object detection, an improved YOLOv5 model is proposed for intelligent construction waste sorting and is trained by a dataset consisting of 3046 construction waste images of, for example, bricks, wood, stones, and plastics. The following conclusions can be drawn:


**Author Contributions:** Project administration, Q.Z.; methodology, H.L.; Data collection, Y.Q. and W.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the "Research Project of the Ministry of Housing and urbanrural Development of the People's Republic of China, grant number 2022-K-079".

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** All data used in this research can be provided upon request.

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**


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