Abstract: In order to improve the performance of object detection algorithm in dynamic traffic scenarios, a moving traffic object detection method based on Bayesian theory fusion is proposed. To obtain initial object detection results, adaptive coordinate attention YOLO (ACA-YOLO) network with high accuracy and multi-scale optical flow (MSOF) method with high sensitivity to dynamic object are applied, respectively. To enhance the detection performance of YOLOv5 network, adaptive coordinate attention (ACA) mechanism is applied to obtain more accurate location and identification of interest objects. To address the issue of constant loss values when the inclusion relationship between truth boxes and prediction boxes occurs, the loss function now utilizes efficient-IoU instead of generalized-IoU. Fusion weights are obtained by calculating the posterior probabilities of ACA-YOLO network and MSOF method separately using Bayesian formula after object detection regions matching based on intersection over union (IoU). The results of fusion detection are based on the posteriori probabilities. The proposed detection method was tested on the KITTI dataset and self-built continuous moving traffic objects dataset which consists of real continuous dynamic traffic scenarios. The experimental results indicate that the proposed method for detecting moving traffic object based on Bayesian theory fusion has outstanding performance. The mean average precision, Recall and Precision of the proposed method on KITTI dataset reach 0.954, 0.912, 0.944, which are 5.5%, 9.9% and 1.9% higher than that of traditional YOLOv5 network.