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%.