基于改进YOLOX的轻量型垃圾分类检测方法

Lightweight Garbage Detection Method Based on Improved YOLOX

生活垃圾分类是保护生态环境、促进绿色和谐发展的有效措施。针对移动端设备计算资源和内存有限,重量级模型难以嵌入等问题,本文提出一种基于改进YOLOX-tiny轻量型的垃圾分类检测方法。首先,使用EIoU替换原来的IoU损失函数,能加速收敛,提升检测精度;其次,在颈部网络引入注意力机制CBAM,对不同通道的权重重新分配,获取更多浅层的细粒度特征和深层的语义信息;最后,使用GhostBottleneck模块替换特征提取网络中的CSP模块,保留更多边缘信息,同时降低参数量,使模型轻量化。在华为云垃圾数据集上的实验结果表明,改进的算法与YOLOX-tiny相比,参数量降低至原来的87.97%,精度提升了0.3个百分点,在TrashNet数据集上的实验效果提升了0.36个百分点,从而证明了本文算法的有效性,该算法有利于嵌入移动端设备使用,具有一定的实用价值。

Household garbage classification is an effective measure to protect the ecological environment and promote green and harmonious development.Aiming at the problems such as limited computing resources and memory,and difficulty in embedding heavyweight models into mobile devices,a lightweight garbage classification detection method based on improved YOLOX-tiny is proposed in this paper.Firstly,the original IoU loss function is replaced by EIoU,which can accelerate the convergence and improve the detection accuracy.Secondly,the attention mechanism CBAM is introduced into the neck network to redistribute the weight of different channels to obtain more shallow fine-grained features and deep semantic information.Finally,the GhostBottleneck module is used to replace the CSP module in the feature picking network,which tends to retain more edge information,reduce the number of parameters,and lighten the model.Experimental results on Huawei cloud garbage dataset show that compared with YOLOX-tiny,the number of parameters of the improved algorithm is reduced to 87.97%of the original,the accuracy is increased by 0.3%,and the experimental effect on TrashNet dataset is increased by 0.36%,which proves the effectiveness of the proposed algorithm.The algorithm is conducive to the use of embedded mobile devices and has certain practical value.