基于改进Mask R-CNN的生活垃圾检测

近年来，人们对于垃圾的分类与回收越来越重视,但垃圾分类耗费了大量的人力和物力且分拣效率低下。针对基于矩形边界框的垃圾检测方法在多分类环境下效果不够理想等问题,提出了一种基于改进Mask R-CNN算法的生活垃圾检测模型。该模型摒弃了传统的ResNet，采用改进的ResNeXt101 作为主干网络进行特征提取，提高了目标检测的准确率和背景边界线分割的精确度。实验结果表明，与传统的Mask R-CNN算法相比，本文模型的mAP为91.1%,提升了2.35%；与当前流行的目标检测模型进行了对比，本文模型的分类准确率和分割精确度均表现优异，表明了所提模型在垃圾检测任务中的可行性与有效性。

Garbage detection based on Mask R-CNN

In recent years, People pay more and more attention to garbage classification and recycling, but garbage classification consumes a lot of manpower and material resources and the sorting efficiency is low. To solve the problem that the garbage detection method based on rectangular bounding box is not effective enough when applied to multi-classification environment, a garbage detection method based on improved Mask R-CNN is proposed. Instead of the traditional ResNet, this method uses the improved ResNeXt101 as the backbone network for feature extraction, which improves the accuracy of object detection and the accuracy of background boundary segmentation. Experimental results show that compared with the traditional Mask R-CNN model, the proposed model’s average classification accuracy is 91.1%, improved by 2.35%. Finally, the experimental comparison with the current popular object detection algorithms shows that the classification accuracy and segmentation accuracy of the proposed algorithm are excellent, which proves the feasibility and effectiveness of the proposed method in the garbage detection task.