TY - JOUR
TI - Enhanced YOLOv8-based detection of surface damages on conveyor belts with improved accuracy and efficiency
AU - Mei Xiuzhuang
AU - Wang Xingtan
AU - Xu Gang
AU - Wang Ying
JN - Thermal Science
PY - 2026
VL - 30
IS - 2
SP - 919
EP - 927
PT - Article
AB - Damage to the surface of composite conveyor belts is a prevalent issue that  can lead to functional failure. In this study, the YOLOv8 algorithm is  enhanced. The convolutional block attention module attention mechanisms and  deformable convolutions are introduced, and a small-object detection layer  is added. The Focaler-CIoU loss function is also employed. These  enhancements are designed to enhance the recognition of damage. In the  context of complex operational conditions, the findings indicate a mean  recognition accuracy of 93.8% for conveyor belt surface damage, thereby  substantiating the efficacy of the enhanced algorithm.
DO - 10.2298/TSCI2602919M
ER -