Minimum bending radius needs to be strictly controlled when laying cables. Accurate segmentation of cable laying image is the basis of controlling bending radius. Traditional visual and classical semantics segmentation methods do not work well for target segmentation of long and thin cables in complex environment. This paper presents a new cable semantics segmentation method based on improved dualmode fusion semantics for ESANet network. Instead of the RGBD Fusion module in ESANet, an efficient SAGate is used to complete the dualmode feature correction and fusion tasks. The fused features participate in the feature extraction of the subsequent two modes at the same time to achieve accurate segmentation of the thin feature cable mask. By collecting RGB and corresponding depth images of cables with different postures, the results show that the improved ESANet network has a good segmentation effect on slender feature targets such as cables, which is 399% higher than Net model segmentation accuracy (mIoU), and 7.68% higher than SwiftNet singlemode semantics segmentation network of RGB. This method can be extended to other target segmentation tasks with slender feature.