基于改进的YOLOv5的户外垃圾检测识别  

Outdoor Garbage Detection Based on Improved YOLOv5

作　　者：陈胜选 王爱民[1] Chen Shengxuan;Wang Aimin(School of Instrument Science and Engineering,Southeast University,Nanjing 210096,Jiangsu,China)

机构地区：[1]东南大学仪器科学与工程学院,江苏南京210096

出　　处：《激光与光电子学进展》2023年第22期70-77,共8页Laser & Optoelectronics Progress

摘　　要：随着垃圾污染问题日益严重,垃圾自动检测识别具有越来越重要的应用价值。改进了YOLOv5算法,提升了对户外复杂背景下垃圾的检测性能,收集了6个类别的户外常见垃圾的图片,建立了一个背景复杂的垃圾图片数据集,提出了一种简单、高效的方法用于生成图片中垃圾目标物的简易真值热力图。基于YOLOv5网络,以真值热力图为量化标准,设计并实验得出一种用于生成垃圾目标物预测热力图的分支结构。随后,将预测热力图送回YOLOv5的backbone结构,增加目标检测网络前向传播过程中特征图的空间注意力权重,以提高整个目标检测网络的性能,改进后的网络仅增加了少量参数,生成了效果可观的预测热力图,垃圾检测的性能得到较大提升。Owing to the increasing severity of garbage pollution,automatic garbage detection has become significantly important in practice.The detection mechanism of YOLOv5 is improved in this study to achieve better performance in outdoor garbage detection against a complicated background.Moreover,here,a garbage dataset is constructed comprising six garbage image types collected in a complex background;subsequently,a simple yet efficient method is proposed to generate ground truth heat maps of garbage objects presented in the images.We treat the corresponding heat maps as a quantization standard and then obtain a branch structure based on YOLOv5 by conducting experiments to generate predicted heat maps.Subsequently,the predicted heat maps are sent back to the backbone structure of YOLOv5 to increase the spatial attention weights of the feature maps in the training process to improve the performance of the entire target detection network.Only a few parameters are added to the improved network,which generates proper predicted heat maps and the performance of garbage detection has been greatly improved.

关 键 词：机器视觉 图像处理 目标检测 垃圾识别 神经网络训练 热力图 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] X705[自动化与计算机技术—计算机科学与技术]