School of Mechanical Engineering, Nanjing University of Science and Technology, 210094 Nanjing, China.
School of Mechanical Engineering, Nanjing University of Science and Technology, 210094 Nanjing, China.
Waste Manag. 2019 Apr 1;88:170-181. doi: 10.1016/j.wasman.2019.03.032. Epub 2019 Mar 25.
Recycling of used plastic bottles is an important measure to protect the environment and save energy. Usually, bottles in different colors have different value for recycling. Classification of plastic bottles recycling based on image recognition during recycling is an effective way, where the position and color recognition are the key technologies. To classify the plastic bottles on the conveyor belt, their position relationships are firstly defined as three categories, i.e. disjoint, adjacent and overlapping. The disjoint ones can be easily identified by the ratio of concave and convex area based on their image. For the adjacent and overlapping bottles, a combination method called distance transformation and threshold segmentation is proposed to distinguish their position relationships. Once the adjacent bottles are identified, the method of concave point search based on convex hull will be used to separate the adjacent recycled bottles further. Then, the color of both the disjoint and adjacent bottles is identified because it is too complex and difficult to recognize color of and separate the overlapping bottles. In the aspect of color recognition, the colors of recycled bottles are divided into seven categories in the sorting process. Color features of the bottom section are used to represent the one of the recycled bottle because there may be a bottle cap and a label on the top and in the middle of the bottle, respectively, resulting in the wrong recognition. ReliefF algorithm is applied to select color features of recycled bottles and the color is identified by support vector machine (SVM) algorithm. The influence of training sample size on classification model is studied and the experimental results show that the accuracy of color recognition of recycled bottles reach 94.7%.
废旧塑料瓶的回收利用是保护环境和节约能源的重要措施。通常，不同颜色的瓶子具有不同的回收价值。基于图像识别的塑料瓶回收分类是一种有效的方法，其中位置和颜色识别是关键技术。为了对传送带上的塑料瓶进行分类，首先将它们的位置关系定义为三类，即不相交、相邻和重叠。不相交的瓶子可以根据其图像的凹凸面积比轻松识别。对于相邻和重叠的瓶子，提出了一种距离变换和阈值分割的组合方法来区分它们的位置关系。一旦识别出相邻的瓶子，就会使用基于凸壳的凹点搜索方法来进一步分离相邻的回收瓶子。然后，识别不相交和相邻瓶子的颜色，因为识别和分离重叠瓶子的颜色过于复杂和困难。在颜色识别方面，在分拣过程中，将回收瓶的颜色分为七类。使用瓶底部分的颜色特征来代表回收瓶的颜色，因为瓶顶和瓶身中部可能分别有瓶盖和标签，导致识别错误。应用 ReliefF 算法选择回收瓶的颜色特征，并通过支持向量机（SVM）算法进行颜色识别。研究了训练样本大小对分类模型的影响，实验结果表明，回收瓶颜色识别的准确率达到 94.7%。
