基于多尺度与混合注意力机制的苹果目标检测

Apple target detection based on multi-scale and hybrid attention mechanism

自然环境下,苹果采摘机器人进行采摘任务时易受到环境因素的影响,导致其视觉系统的检测性能降低,出现误检和漏检的情况.针对此问题,在YOLOv3的基础上,提出了一种基于多尺度与混合注意力机制的目标检测模型PM-YOLOv3.首先,改进YOLOv3的特征提取网络,减少网络残差模块的数量,引入多尺度卷积,构建新的特征提取网络;然后,添加注意力机制模块,强化重要的特征信息,忽略无关的信息;最后,对先验框进行改进.实验结果表明:改进的PM-YOLOv3检测模型在测试集上的F1值可达到93.6%,有明显的提高,能满足自然环境下对苹果目标的准确检测.

In the natural environment,the apple picking robot is affected by environmental factors when performing picking tasks,which leads to the detection performance decrease of its vision system,false detection and missed detection.To solve this problem,a target detection model PM-YOLOv3 based on multi-scale and hybrid attention mechanism was proposed.Firstly,the feature extraction network of YOLOv3 was improved,the number of residual module of the network is reduced,multi-scale convolution is introduced,the new feature extraction network is builded.Secondly,the attention mechanism module is added to strengthen important feature information,irrelevant information is ignored.Finally,the anchor box was improved.Experimental results showed that the F1 value of the PM-YOLOv3 detection model on the test set can reach 93.6%,which is significantly improved,it can meet the accurate detection of apple targets in the natural environment.