【摘要】 为解决传统巡检图像检测方法对电力设备小目标缺陷检测能力弱、错检和漏检率高、浅层网络语义信息不足等问题，提出针对电力设备小目标缺陷的多尺度特征融合增强检测模型（multi-scale feature fusion enhanced youonlylookonce,MFFE-YOLO）。该方法设计了一种多尺度特征融合增强机制（multi-scalefeaturefusion enhancement,MFFE），能够更全面地捕捉目标特征。研究表明：在C2f-EF模块中嵌入跨空间学习多尺度注意力机制EMA以及FasterNet Block，能够优化模型的运行效率；MFFE-YOLO方法的平均精度、参数量和帧率指标均优于其他方法，能够实现高精度与实时性之间的良好平衡。更多还原

【Abstract】 In order to solve the problems of weak detection ability of small target defects of power equipment, high false detection and missed detection rate, and insufficient shallow network semantic information in traditional inspection image detection methods, a feature fusion enhanced detection method for small target defects of power equipment(multi-scale feature fusion enhanced you only look once, MFFE-YOLO) is proposed. This method designs a multi-scale feature fusion enhancement mechanism(multi-scale feature fusion enhancement, MFFE), which can capture target features more comprehensively. The study shows that embedding the cross-space learning multi-scale attention mechanism EMA and Faster Net Block in the C2f-EF module can optimize the operation efficiency of the model; the average accuracy, parameter amount and frame rate indicators of the MFFE-YOLO method are better than that of other methods, and can achieve a good balance between high accuracy and real-time performance.更多还原