基于改进Faster R-CNN算法的垃圾分类与识别应用

Application of waste classification and recognition basedon improved Faster R-CNN algorithm

在日常生活中,垃圾分类主要靠人工处理,存在效率低、易错和工作繁重等问题,为了解决这一难题,本文提出了一种改进的Faster R-CNN算法,在垃圾图像识别检测任务中效果表现较好.相较于传统的Faster R-CNN算法,它的关键改进是使用ResNet50网络取代了原有的基础卷积神经网络作为算法中的backbone部分.实验结果表明,改进后的Faster R-CNN算法较传统的Faster R-CNN算法具有更强的垃圾分类能力,这项研究为垃圾分类问题提供了一种更加可行和有效的解决方案,有望在环境保护和资源回收方面发挥重要作用.

In daily life,the work of rubbish classification mainly relies on manual processing,and this method has problems such as low efficiency,error-prone and heavy workload.In order to solve this problem,this paper proposes an improved Faster R-CNN algorithm,which performs better in the rubbish image recognition detection task.Compared with the traditional Faster R-CNN algorithm,its key improvement is the use of ResNet50 network instead of the original base convolutional neural network as the backbone part of the algorithm.The experimental results show that the improved Faster R-CNN algorithm has a stronger ability to classify rubbish than the traditional Faster R-CNN algorithm.The research provides a more feasible and effective solution to the problem of rubbish classification,which is expected to play an important role in environmental protection and resource recovery.