Waste drilling fluid flocculation identification method based on improved YOLOv8n.

Abstract

Efficient identification of the flocculation state of waste drilling fluid remains a significant challenge. This study proposes an improved You Only Look Once version 8 nano-algorithm (YOLOv8n), specifically optimized for real-time monitoring of drilling fluid flocculation under field conditions. The algorithm employs MobileNetV3 as the backbone network to minimize memory usage, improve detection speed, and reduce computational requirements. The integration of the efficient multi-scale attention mechanism into the cross-stage partial fusion module effectively mitigates detail loss, resulting in improved detection performance for images with high similarity. The wise intersection over union loss function is employed to accelerate bounding box convergence and improve inference accuracy. Experimental results show that the enhanced YOLOv8n algorithm achieves an average recognition accuracy of 98.6% on the experimental dataset, a 4.8% improvement over the original model. In addition, the model size and parameter count are reduced to 2.9 MB and 2.8 Giga Floating-Point Operations Per Second (GFLOPS), respectively, compared to the original model, reflecting a reduction of 3.2 MB and 5.3 GFLOPS. As a result, the proposed flocculation recognition algorithm is highly deployable and effectively predicts flocculation state changes across varying working conditions.