关键词: 井场泄露, 小目标检测, 特征融合, 注意力机制

Abstract: The leakage of well site pumping units is an important issue that affects the safety production and stable operation of oil fields. The current object detection methods often overlook the special requirements of well site leakage detection, and there are some limitations in the process of feature recognition of well site leakage targets. An attention mechanism leak detection algorithm for small targets in well sites is proposed based on the YOLOv5(You Only Look Once 5) network, introducing channels and spatiotemporal attention modules into the backbone network, to obtain more feature discrimination information, to enhance the model’s attention to important features. An additional small object detection scale has been introduced in the backbone network, which enables the network to integrate more feature information of small target objects and enhance the detection ability of small targets. The effectiveness of the proposed algorithm is validated on a dataset of well site leaks. The experimental results showed that compared to similar algorithms, the proposed method has higher recognition accuracy and can provide reference for the practical application of automatic detection of oil field well site leaks. 

Key words: well site leakage, small target detection, feature fusion, attention mechanism