To address challenges such as time consumption, interference from dynamic objects, insufficient feature points leading to low real-time performance, reduced mapping accuracy, and inaccurate pose estimation in indoor dynamic environment mapping of visual SLAM (simultaneous localization and mapping) systems, this study proposes a visual SLAM algorithm based on object detection and point-line feature association, referred to as LDF-SLAM. To mitigate time consumption and dynamic object interference, MobileNetV3 is introduced to replace the YOLOv8 backbone network, thereby reducing the number of network parameters. A parameter-free attention-enhanced ResAM module is designed and integrated with the MobileNetV3 network to create a lightweight detection network to enhance detection capability and efficiently identify dynamic objects. Subsequently, the multi-view geometry method is introduced to compensate, filter and reject potential dynamic feature points together with the improved lightweight network, and the remaining static feature points are used to construct a dense point cloud map, thereby improving the mapping accuracy of the SLAM system. In addition, to resolve inaccuracies in pose estimation due to insufficient static feature points, a fusion FLD line feature extraction method is proposed to enhance pose estimation accuracy. A line segment length suppression mechanism is also designed to ensure the system's real-time performance and improve its robustness. Experiments conducted on the TUM and Bonn data sets demonstrate that the root-mean-square-error (RMSE) of absolute trajectory error of LDF-SLAM is reduced and outperforms other mainstream SLAM algorithms, significantly enhancing the robustness and accuracy of the SLAM system in dynamic environments.