Aiming at the problem of online monitoring of the obstruction flow state on the Shipai curved section,the application of image target detection is studied.The existing target detection algorithms are rarely used in the field of flow state recognition.Therefore,the flow state feature data are collected in the target section,and the surface obstruction flow state dataset SOFSD is self-made.To ensure the detection speed and accuracy,based on the YOLOv5s framework,combined with the CA(coordinate attention)and the BiFPN（bidrectional feature pyramid network）the YOLOv5s-CA-BiFPN model is constructed,and a new intelligent recognition method for the obstruction flow state on the channel surface is proposed based on the model.The experimental results show that the YOLOv5s-CA-BiFPN model improves the accuracy and recall rate by 2.3% and 0.8% respectively compared with YOLOv5s,and the mAP@0.5 is increased by 1.3% and mAP@0.5:0.95 is decreased by 2.2%.It is superior to YOLOv5s in both detection effect and generalization performance,effectively reducing missed detection and false detection,and improving the small target detection ability.Finally,an intelligent recognition system for obstructive flow on the waterway surface is constructed based on this method,which can provide reference for the construction of smart waterways.