SOLOv2-cable: A Power Cable Segmentation Algorithm in Complex Scenarios

In this paper, we propose SOLOv2-cable: a power cable segmentation algorithm based on neural network in complex scenarios, to solve the problems of miss predictions in the traditional algorithm in the process of power cable identification. The method is based on the SOLOv2 algorithm. First, the attention mechanism is introduced, and the ResNeSt network with the separation attention mechanism is used to design the backbone network to obtain multi-scale feature representation. Second, we optimize the loss function by adding the coordinate information of the instant center to the loss function, replace focal loss with GHM loss, and discuss the potential of GHM in imbalanced datasets; finally, data enhancement and cosine annealing step size are used to further improve the generalization ability of the model. Compared with the original SOLOv2 algorithm, the AP, AP50, and AP75 of SOLOv2-cable are improved by 6.4%, 7.1%, and 8.2%, and the reasoning time is only increased by 17%. This work can better identify power cables and provide technical support for subsequent laying of cables and measurement of cable curvature.