Insulator Defect Detection Model Based on Improved Faster-RCNN
Aiming at the problem of low accuracy in identifying insulator defects under complex outdoor backgrounds,this paper proposes an improved Faster-RCNN algorithm,which uses ResNet50+FPN to replace the original backbone net-work and introduces the CBAM attention mechanism into the network.Then the K-mean algorithm was used to customize the size of the anchor box to improve the detection accuracy of small targets.Transfer learning is performed using weights trained on the COCO dataset.Experimental results show that compared with the original algorithm,the improved model improves the average detection accuracy of various types of insulator defects by 4.4%,which has certain engineering refer-ence value.
万方数据
维普
