Deep learning-based detection of construction waste in complex scenarios with an improved lightweight algorithm
YiZhong Yang, YeXue Li, ShengWei Li, MaoHu Tao, Xuzhi Chen · 2026
Read Deep learning-based detection of construction waste in complex scenarios with an improved lightweight algorithm by YiZhong Yang, YeXue Li, ShengWei Li, MaoHu Tao, Xuzhi Chen (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13588414.txt.
Description
YiZhong Yang and co-authors built a lighter computer vision system for spotting construction waste in messy scenes, in a paper published in 2026. Rubble piles mix shapes and overlap, so standard detectors miss items or run too slowly for live use. Public data sets for this task are also scarce.
Their system, GTS-YOLO, trims a recent detector design, adds a focus step that weighs important image areas, and reshapes the final detection stage. On their own ten-class data set it scored higher than the base model while using about a fifth fewer parts, and the file shrank to just over four megabytes. Tests on public data sets also went well. The balance of speed and accuracy suits on-site sorting tools.
Catalog record
| CREATOR | YiZhong Yang, YeXue Li, ShengWei Li, MaoHu Tao, Xuzhi Chen |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 96 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |