计算机工程与应用2024，Vol.60Issue(5)：250-258,9.DOI:10.3778/j.issn.1002-8331.2210-0235
改进YOLOX-s的密集垃圾检测方法
Improving YOLOX-s Dense Garbage Detection Method
摘要
Abstract
To address the problems of low recognition rate,inaccurate localization and false detection and omission of tar-gets to be detected in densely stacked multi-species garbage detection,a garbage detection method in corporating multi-headed self-attention mechanism to improve YOLOX-s is proposed.Firstly,the Swin Transformer module is embedded in the feature extraction network,and the multi-headed self-attention mechanism based on the sliding window operation is introduced to make the network take into account the global feature information and the key feature information to reduce the false detection phenomenon.Secondly,the deformable convolution is used in the prediction output network to refine the initial prediction frame and improve the localization accuracy.Finally,on the basis of the EIoU,loss weighting coeffi-cients are introduced to propose a weighted IoU-EIoU loss,which adaptively adjusts the degree of concern for different losses at different stages of training to further accelerate the convergence of the training network.Testing on a public 204-class spam detection dataset,the results show that the average mean accuracy of the propose improve algorithm can reach 80.5% and 92.5% ,respectively,which is better than the current popular target detection algorithms,and the detection speed is fast to meet the real-time requirements.关键词
密集垃圾检测/多头自注意力机制/YOLOX-s/深度学习Key words
dense spam detection/multi-head self-attention mechanism/YOLOX-s/deep learning分类
信息技术与安全科学引用本文复制引用
谢若冰,李茂军,李宜伟,胡建文..改进YOLOX-s的密集垃圾检测方法[J].计算机工程与应用,2024,60(5):250-258,9.基金项目
国家自然科学基金(62271087). （62271087）
