Question 1

What problem does PHL-YOLO target in yarn production?

Accepted Answer

It targets the need for real-time and high-precision yarn anomaly/quality detection during actual yarn manufacturing, where manual inspection is labor-intensive and expertise-dependent.

Question 2

How does PHL-YOLO improve the original YOLOv8 architecture?

Accepted Answer

It adds the PEC2F module for stronger feature extraction with lower computation, uses HWD for efficient downsampling, replaces the YOLOv8 detection head with LSCDH to simplify computation, and applies GSPMD structured pruning to further reduce model complexity.

Question 3

What experimental gains are reported compared with the original YOLOv8?

Accepted Answer

The model achieves a 1.0% increase in precision, a 2.5% improvement in recall, and a 2.3% rise in mAP, alongside a 26 FPS performance boost; computational load and parameters are reduced to 29.6% and 10.2%, respectively.