Enhancing waste classification accuracy with Channel and Spatial Attention-Based Multiblock Convolutional Network

Municipal waste classification is significant for effective recycling and waste management processes that involve the classification of diverse municipal waste materials such as paper, glass, plastic, and organic matter using diverse techniques. Yet, this municipal waste classification process faces several challenges, such as high computational complexity, more time consumption, and high variability in the appearance of waste caused by variations in color, type, and degradation level, which makes an inaccurate waste classification process. To overcome these challenges, this research proposes a novel Channel and Spatial Attention-Based Multiblock Convolutional Network for accurately classifying municipal waste that utilizes a unique attention mechanism for enhancing feature learning and waste classification accuracy. In this research, the data augmentation technique is utilized to improve the size and diversity of the images, which creates a new municipal waste image from the existing image. After the augmentation process, the data preprocessing is performed through normalization, resizing, and dividing the images into smaller patches. In the feature extraction phase, each image patch's detailed representation is created by integrating and extracting the image's relevant features from the embedding space. Finally, the proposed Channel and Spatial Attention-Based Multiblock Convolutional Network predicts the types of municipal waste that are represented in each image patch by classifying the input images into diverse kinds of waste categories. The experimental validation exposed that the proposed Channel and Spatial Attention-Based Multiblock Convolutional Network effectively classifies the municipal waste images and achieves a higher accuracy of 98.73%, lower mean absolute error of 0.048, and lower root mean square error of 0.087 when compared to existing municipal waste classification strategies. These results prove that the proposed Channel and Spatial Attention-Based Multiblock Convolutional Network framework is more accurate, reliable, and well-suited to real-time municipal waste classification.