Aiming at the problems that the existing deep learning based visual simultaneous localization and mapping (visual SLAM) loopback detection methods do not make good use of the semantic information of images,scene details and poor real-time performance in complex outdoor scenes with obvious illumination changes or occlusion objects,this paper proposes an YOLO-NKLT visual SLAM loopback detection method.The YOLOv5 network model with improved loss function is used to obtain image features with semantic information,construct the training set,and retrain the network to make the extracted features more suitable for loopback detection in complex scenes.In order to further improve the real-time performance of closed-loop detection,a KLT dimensionality reduction method based on non-dominated sorting is proposed.Through experiments on the New College dataset and Nordland dataset with more complex changes such as illumination,the results show that compared with other traditional and deep learn-based methods in outdoor complex scenes,it has higher robustness and can achieve better accuracy and real-time rate performance.