基于机器学习One-stage目标检测算法的塑料自动识别系统

Automatic Plastic Detection System Based on One-Stage Machine Learning Algorithm on Object Detection

针对当前日益严重的塑料污染问题,对于塑料垃圾的检测识别,开发了基于机器学习One-stage目标检测领域中YOLOv2算法的塑料自动识别系统。系统构建了目标检测的神经网络,结合塑料垃圾公开数据集进行训练,从而实现实时塑料垃圾检测网络系统。同时,采用塑料垃圾数据进行系统测试,设置分样本准确率、召回率、综合平均精确率等评估参数进一步实现对系统的评估。结果表明:基于机器学习One-stage目标检测算法的塑料自动识别系统能够有效完成识别任务,综合平均精确率在87.3%左右,可以快速准确地将环境中的塑料从较为复杂的自然环境中检测出来,对解决塑料污染有较好的实际意义。

In response to the increasingly serious plastic pollution problem,for the detection and identification of plastic waste,an automatic plastic identification system based on the YOLOv2 algorithm in the field of machine learning One-stage target detection has been developed.The system constructs a neural network for target detection,and trains with the public plastic waste data set to realize a real-time plastic waste detection network system.At the same time,the plastic waste data is used for system testing,and evaluation parameters such as sub-sample accuracy,recall rate,and comprehensive average accuracy rate are set to further realize the evaluation of the system.The results show that:the plastic automatic identification system based on the machine learning One-stage target detection algorithm can effectively complete the identification task,with a comprehensive average accuracy rate of about 87.3%,and can quickly and accurately detect plastics in the environment from a more complex natural environment,Has good practical significance for solving plastic pollution.