Aiming at the low accuracy problem of existing algorithms for fine-grained target bird recognition tasks， a target detection algorithm for bird targets called YOLOv5-Bird， was proposed. Firstly， a mixed domain based Coordinate Attention （CA） mechanism was introduced in the backbone of YOLOv5 to increase the weights of valuable channels and distinguish the features of the target from the redundant features in the background. Secondly， Bi-level Routing Attention （BRA） modules were used to replace part C3 modules in the original backbone to filter the low correlated key-value pair information and obtain efficient long-distance dependencies. Finally， WIoU （Wise-Intersection over Union） function was used as loss function to enhance the localization ability of algorithm. Experimental results show that the detection precision of YOLOv5-Bird reaches 82.8%， and the recall reaches 77.0% on the self-constructed dataset， which are 4.3 and 7.6 percentage points higher than those of YOLOv5 algorithm. Compared with the algorithms adding other attention mechanisms， YOLOv5-Bird also has performance advantages.It is verified that YOLOv5-Bird has better performance in bird target detection scenarios.