Aiming at the problems of low timeliness of single centralized data processing in the cloud, low accuracy of bird’s nest detection on overhead lines, high consumption of model’s arithmetic power on edge computing devices, and inaccurate target localization, an algorithm for detecting and localizing bird’s nests on overhead lines based on the collaboration of cloud-edge and end-end is proposed. The algorithm solves the problem of low efficiency of centralized processing in the cloud through the collaboration of cloud, end and edge, and solves the problem of unclear images due to angle and light through the collaboration of cloud-edge data visualization. In order to improve the accuracy of bird’s nest detection on overhead lines, the algorithm is optimized on the basis of YOLOv5x model. First, by replacing the C3 module in the backbone feature extraction network with the C2f module, and adding the SE (squeeze and excitation) attention module in the last layer to improve the model’s ability to detect small targets. Secondly, the activation function is replaced with the Mish function to solve the problem of neurons stopping learning due to the saturation of the training gradient. In order to reduce the model’s consumption of computing power on edge computing devices, the improved model is pruned and fine-tuned to reduce the scale of model parameters. Based on this optimized model, a 3D target localization algorithm is proposed, and the localization results are corrected by combining with the GIS (geographic information system) system, which achieves accurate localization of the detected target. The experimental data show that the mean average accuracy of the improved model reaches 93.25%, which is 3.44% higher than the original YOLOv5x model, and the pruning rate of the optimized model reaches 45%. The detection target is able to locate to the corresponding pole tower after 3D spatial modeling calculation and position correction, which effectively guides the staff to quickly and accurately eliminate hidden dangers.