The open-pit mining area is a complex scene，and the traveling obstacle detection is seriously interfered by dust noise such as dust and particles，which makes it difficult to accurately identify obstacles，especially at night when the light is poor，which is not conducive to correct decision-making，thus affecting the safety and overall efficiency of unmanned operation.In view of the above problems，a YOLOv8n-based YOLOv8n-Enhanced algorithm for detecting traveling obstacles in open-pit mining areas was proposed.The algorithm is mainly improved in three aspects：Firstly，for the problems of serious interference by dust noise and poor light at night，a C2fCA module structure was proposed instead of the original C2f module，which utilizes the shared weights and context-aware weights to enhance the dependency relationship between different locations of the image，mitigate the noise interference，and improve the feature extraction ability of the model.Secondly，to trade-off the accuracy and real-time performance of the open-pit obstacle detection model，the Neck end of the model was reconstructed，and the lightweight convolutional techniques GSConv and VoV-GSCSP modules were used to reduce the complexity of the computation and network structure，and realize a higher computational cost-effectiveness of the detector.Finally，for the situation that there is a large gap between the quality of data in the open-pit mining area，especially at night when there is insufficient light，and low-quality data will affect the ability of the model to learn features in training，the loss function was optimized to solve the problem of the bounding box regression equilibrium between the samples of different qualities，to improve the ability of the model to generalize and accelerate the convergence.The experimental results show that the improved algorithm in this paper reduces the computational GFLOPs of the model by about 8.5% and the number of parametric params by about 3% while maintaining the real-time performance，and improves the mean Average Precision（mAP） of the YOLOv8n by 1.8% and 2.6% in daytime and nighttime scenarios，respectively，and realizes obstacle recognition at different scales in daytime and nighttime scenes.