Abstract
Waste detection and classification are critical processes in modern waste management systems, as they enable the efficient sorting and processing of various waste types. The significance of effective waste detection lies in its potential to minimize environmental impact and promote sustainability. With the growing volumes of urban waste, implementing advanced detection technologies is essential to facilitate timely interventions and optimize waste handling practices. For effective waste detection, several Deep Learning-based techniques have been implemented. Yet, they are limited by several drawbacks including poor accuracy, generalizability, computational efficiency, and class imbalance issues. This research work develops an automated methodology that incorporates Deep Convolutional Neural Networks with Adaptive Boosting for accurate waste detection and categorization. This study deployed a data augmentation step to enhance the quantity of images and resolve class imbalance problems. Deep Convolutional Neural Networks automatically identify and learn relevant features from raw image data, such as edges, textures, and shapes, which are crucial for distinguishing between different types of waste. The proposed model introduces an Adaptive Boosting ensemble learning technique for enhancing the classification performance by combining the outputs of several weak classifiers. Then, the proposed technique adopted a Binomial Crossover Ship rescue algorithm that incorporates the Ship Rescue Optimization algorithm with the Binomial Crossover Strategy that fine-tunes the hyperparameters of the proposed technique and improved the overall effectiveness of the model. In addition, the effectiveness of this study is evaluated using several waste detection datasets with distinct performance measures and the model attains superior detection results such as accuracy of 98.82% and precision of 98.56%. The simulation findings show that the proposed method provides an excellent contribution to early and accurate waste detection systems.