The proportion of armor clamp rust in aerial images of power transmission lines is rich in details and irregularly distributed. To overcome problems such as local information loss, low accuracy, and slow speed in the segmentation detection process, a DeepLabV3+- based semantic segmentation model for armor clamp rust is proposed. The backbone network is replaced with a lightweight improved MobileNetV3 network to speed up computation, and an adaptive feature pyramid (AFP) structure is proposed to merge multiple scales. A feature fusion atrous spatial pyramid pooling ( FEF-ASPP) structure is proposed, combined with the FRN layer to strengthen pixel relationships without reducing resolution. Finally, the loss function is optimized to improve the effectiveness of the operator. Experiments show that the mIoU and mPA reach 87. 15% and 96. 64%, respectively, which is an improvement of 3. 09% and 4. 29% compared to the original model. The parameter quantity is only 48% of the original model, and the inference time is only 15. 94 ms, reducing the requirement for device computing power and achieving high-efficiency, high-precision, and lightweight segmentation detection of armor clamp rust in power transmission equipment.