垃圾分类AI识别应用数据

垃圾分类准确率是反映居民投放的厨余垃圾中掺杂其他垃圾的成分或可回收物中不可回收物的成分，其他或不可回收垃圾成分越高准确率约低。首先，企业或政府单位可根据垃圾分类准确率了解各小区居民多垃圾分类意识的整体水平、变化趋势，针对性地进行宣传指导，提高垃圾分类知晓率。其次，根据垃圾分类准确率能指导运营单位进行有效的人员投入以及对垃圾处理末端企业提供数据支持进行高效处理。数据类型：需要准备标注好的图像数据，其中包含了希望进行分割的区域，如一张图像中易腐部分\非易腐部分\塑料袋部分；数据来源：数据来源平台事件汇总的各类垃圾图像，筛选清洗挑选出可用样本；本算法预期输出数据:输出像素级别的分割掩码，用于标识输入图像中的每个像素属于哪个类别或对象。具体设置预期输出数据的方法如下：（1）标签映射：首先，需要为每个对象或区域分配一个唯一的整数标签。（2）分割掩码创建：创建一个与输入图像尺寸相同的分割掩码。掩码的每个像素值应该与相应位置的像素属于的类别标签相匹配，以表示该像素属于哪个类别。（3）网络架构：本算法采用编码-解码结构，编码器用于提取图像特征，解码器部分用于生成分割掩码。使用交叉熵损失函数,优化器选择的是Adam,训练目标是最小化损失函数以优化网络参数。

The waste sorting accuracy rate reflects the share of non-target waste mixed into the collected waste streams: specifically, the proportion of non-kitchen waste in the categorized kitchen waste, or non-recyclable items in the categorized recyclable waste. The higher the share of such non-target waste, the lower the measured waste sorting accuracy.

First, enterprises or government agencies can use the waste sorting accuracy rate to understand the overall level and changing trends of residents' waste sorting awareness in different residential communities, carry out targeted publicity and guidance, and improve residents' awareness of waste sorting.

Second, the waste sorting accuracy rate can guide operating units to make effective personnel allocation, and provide data support for terminal waste treatment enterprises to carry out efficient waste processing.

Data Type: Annotated image data is required, which contains the regions to be segmented, such as perishable waste, non-perishable waste, and plastic bag regions in a single image.

Data Source: Various types of waste images aggregated from data source platforms, with available samples selected after screening and cleaning.

Expected Output Data of This Algorithm: Pixel-level segmentation masks, which are used to identify which category or object each pixel in the input image belongs to.

Specific settings for generating the expected output data are as follows:
1. Label Mapping: First, assign a unique integer label to each object or region.
2. Segmentation Mask Creation: Create a segmentation mask with the same dimensions as the input image. The pixel value of each position in the mask should match the category label of the corresponding pixel in the input image, indicating which category the pixel belongs to.
3. Network Architecture: This algorithm adopts an encoder-decoder structure. The encoder is used to extract image features, while the decoder is used to generate segmentation masks. The cross-entropy loss function is used, and the optimizer selected is Adam. The training objective is to minimize the loss function to optimize the network parameters.

搜集汇总

数据集介绍

特点

该数据集包含30条垃圾分类AI识别应用数据，每日更新，记录垃圾投放的详细信息，如设备编号、垃圾类型、投放时间及各类垃圾面积占比等，用于分析和提高垃圾分类准确率。