注意力机制优化RetinaNet的密集工件检测方法研究

Research on dense workpiece detection method based on attentional mechanism optimization RetinaNet

针对密集工件存在相似度高、排列无序的特性导致检测难度大的问题,提出了一种注意力机制优化RetinaNet的密集工件检测方法。首先将注意力机制引入到RetinaNet主干特征提取网络以减少干扰物对检测效果的影响,提高神经网络的特征提取能力;然后利用Soft-NMS构建新的预测框提高重叠定位精度;最后通过迁移学习的方法训练数据集,提高模型训练效率。在密集工件数据集上验证该方法的有效性,实验结果表明,改进后的方法检测精度达到了98.11%,相较于改进前提高了2.59%,单张图片检测速度达到了0.026 s,该方法能够满足实际工业生产过程中精准检测工件的目的,在保证速度的同时降低了漏检率和误检率。

In order to solve the problem of difficult detection due to the existence of dense workpiece with high similarity and disorderly arrangement,an attention mechanism is proposed to optimize RetinaNet’s dense workpiece detection method.Firstly,the attention mechanism is introduced into the RetinaNet backbone feature extraction network to reduce the influence of interfering objects on the detection effect and improve the feature extraction ability of the neural network.then a new predictive box is constructed using Soft-NMS to improve the overlap localization accuracy.Finally,the dataset is trained by transfer learning method to improve the model training efficiency.The method effectiveness is verified on the produced dense workpiece dataset;Experimental results show that the detection accuracy of the improved method reaches 98.11%,which is 2.59%higher in comparison with that before the improvement.The detection speed of a single picture is up to 0.026 s.The proposed method can meet the purpose of accurate detection of workpiece in actual industrial production process,which can reduce the rate of missed and false detection and assure the speed simultaneously.