YOLOv8-POS: a lightweight model for coal-rock image recognition.

ABSTRACT: A novel approach, designated YOLOv8-POS, is introduced to address the issue of false detections in coal-rock image recognition tasks, frequently caused by factors such as image defocus, dim lighting, and worker occlusion, and to further enhance the model's accuracy and reduce its complexity. The methodology introduces a C2f-PConv module, which ingeniously combines the strengths of C2f and partial convolution (PConv) to selectively process channels. This reduces unnecessary computational overhead while preserving the integrity of critical feature information, thus significantly cutting down on the model's parameters and computational demands. Additionally, an Overlapping Spatial Reduction Attention module is incorporated into the model's architecture to optimize the fusion of spatial featur

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC12190560 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature