摘要：Abnormal shutdown detection in oilfield pumping units presents significant challenges, including degraded image quality under low-light conditions, difficulty in detecting small or obscured targets, and limited capabilities for dynamic state perception. Previous approaches, such as traditional visual inspection and conventional image processing, often struggle with these limitations. To address these challenges, this study proposes an intelligent method integrating multi-scale feature enhancement and low-light image optimization. Specifically, a lightweight low-light enhancement framework is developed based on the Zero-DCE algorithm, improving the deep curve estimation network (DCE-Net) and non-reference loss functions through training on oilfield multi-exposure datasets. This significantly enhances brightness and detail retention in complex lighting conditions. The DAFE-Net detection model incorporates a four-level feature pyramid (P3–P6), channel-spatial attention mechanisms (CBAM), and Focal-EIoU loss to improve localization of small/occluded targets. Inter-frame difference algorithms further analyze motion states for robust

ISSN号：2227-9717

卷、期、页：卷13期10

发表日期：2025-09-23

影响因子：0.000000

期刊分区（SCI为中科院分区）：四区

收录情况：SCI（科学引文索引印刷版）,EI（工程索引）,SCIE（科学引文索引网络版）

发表期刊名称： PROCESSES

参与作者：谭昆,毛亚明,王顺义

通讯作者：王书婷

第一作者：韩国庆

论文类型：期刊论文

论文概要：谭昆,王书婷,毛亚明,王顺义,韩国庆，Dynamic Anomaly Detection Method for Pumping Units Based on Multi-Scale Feature Enhancement and Low-Light Optimization， PROCESSES，2025，卷13期10

论文题目：Dynamic Anomaly Detection Method for Pumping Units Based on Multi-Scale Feature Enhancement and Low-Light Optimization