基于注意力机制的鸟类识别算法

Bird recognition algorithm based on attention mechanism

针对现有细粒度鸟类目标识别算法准确率不高的问题,提出一种鸟类目标检测算法YOLOv5-Bird。首先,在YOLOv5主干网络中引入基于混合域的坐标注意力(CA)机制,增大有价值的通道权重,以区分目标特征和背景中的冗余特征;其次,在原始主干网络中采用双层路由注意力(BRA)模块替换原网络中的部分C3模块,过滤低相关度的键值对信息,获得高效的长距离依赖关系;最后,使用WIoU(Wise-Intersection over Union)损失函数,增强算法对目标的定位能力。实验结果表明,YOLOv5-Bird在自建数据集上取得了82.8%的精确率和77.0%的召回率,比YOLOv5算法分别提高4.3和7.6个百分点,也优于增加其他注意力机制的算法。验证了YOLOv5-Bird在鸟类目标检测场景中具有较好的性能。

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.