Many complex elements such as poor light and high noise in the underwater environment result in low detection accuracy and high missed detection rate in traditional underwater target detection methods. To address these issues, based on the current general Faster R-CNN algorithm, this study proposes an underwater target detection algorithm based on automatic color level and bidirectional feature fusion. First, the automatic color level was used to enhance a blurred underwater image. Second, the path aggregation feature pyramid network (PAFPN) was introduced for feature fusion to enhance the expression for shallow information. Third, the soft non-maximum suppression (Soft-NMS) algorithm was introduced to modify and generate the candidate target regions before and after training. Finally, the FocalLoss function was used to rectify the issue of an unbalanced distribution of positive and negative samples. The experimental results show that the proposed algorithm can reach a detection accuracy of 59.7% on the URPC2020 dataset and a recall rate of 70.5%, which are 5.5 percentage points and 8.4 percentage points respectively higher than the current general Faster R-CNN algorithm, effectively improving the average accuracy of underwater target detection.