基于轻量级SSD的电力设备锈蚀目标检测

电力设备的锈蚀检测作为电力系统故障检测中非常重要的组成部分,需要被快速准确的识别出来.本文结合注意力模型提出一种基于轻量级SSD的电力设备锈蚀目标检测算法,可以有效地对电力设备的锈蚀区域进行检测.本文算法模型利用深度可分离卷积代替标准卷积来大幅度压缩模型,并在此基础上提出了一种基于注意力模型的上采样特征融合策略用于弥补缩减模型结构带来的精度损失.该算法在RustDetection数据集上相比较标准SSD可以做到在参数量减少63.6%,速度提升46.7%的情况下提升10.47%的准确度和5.99%的平均精度.

Corrosion Object Detection of Power Equipment Based on Lightweight SSD

Corrosion detection of power equipment is a very important part of power system malfunction detection and needs to be quickly and accurately identified. This study proposes an algorithm of power equipment corrosion object detection based on attention model, which can effectively detect the rust area of power equipment. The proposed algorithm model uses the depthwise separable convolution instead of the standard convolution to compress the model greatly. Based on this, an upsampling feature fusion strategy based on the attention model is proposed to compensate for the loss of precision caused by the reduced model structure. Compared with the standard SSD on the RustDetection dataset, the proposed algorithm can improve the accuracy of 10.47% and the average accuracy of 5.99% when the parameter quantity is reduced by 63.6% and the speed is increased by 46.7%.