DOLD-Net: Dense occluded livestock detection via global-local feature collaboration
Kaida Jia, Yang Chai, Baozhou Chen, Zunhai Gao · 2026
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Description
Kaida Jia and co-authors build a vision model that finds farm animals in crowded, blocked views in a 2026 paper. Smart farming needs exact head counts, yet animals pack tight and hide each other. Standard detectors lose features when bodies overlap and edges blur.
Their model, DOLD-Net, pairs local edge flow with global scene shape in a two-branch spine. Added parts spread core scene meaning downward, split tangled signals by frequency, and fuse scales to fill gaps. On four public data sets it struck a strong balance of accuracy and speed. Herd checks in dense pens are the target use.
Catalog record
| CREATOR | Kaida Jia, Yang Chai, Baozhou Chen, Zunhai Gao |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 53 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |