Research on waste drilling fluid flocculation identification algorithm based on improved YOLOv5s

In the identification of waste drilling fluid flocculation, the existing identification methods rely too much on manual experience judgment which have low recognition accuracy and efficiency for flocculation state, and cannot reflect the flocculation state of drilling fluid in real time. In order to achieve the reduction and reuse of waste drilling fluid and improve the efficiency o f oil and gas production, this paper proposes a waste drilling fluid flocculation recognition algorithm based on improved YOLOv5s. Combining the characteristics of the flocculation state of waste drilling fluid and the interference of the field environment, the SE channel attention mechanism is introduced to improve the accuracy of the algorithm, and the EIOU loss function is introduced to solve the imbalance problem of positive and negative samples, thereby improving the target detection performance. The improved SE-YOLOv5s target detection algorithm has increased the accuracy by 4.2% in the flocculation state data set, shortened the model training time, and has good robustness and real-time performance, meeting the real-time requirements of the offshore drilling platform reduction and reuse system.