Efficient Waste Classification in Recycling Systems Using Contrast and Attention Enhanced Deep Learning

Automated waste sorting is essential for making recycling more efficient and reducing pollution. This project implements and tests a deep‑learning system that can classify waste into multiple categories. The core method uses the Class‑Adaptive Color‑Sensitive (CASC) filtering algorithm together with a Dense Class‑Adaptive Color‑Sensitive DenseNet‑121 (DenseCSENet‑121) model. By adding contrast and attention‑enhanced features, the system deals with the visual similarity and color‑sensitivity problems that appear in mixed waste streams. The implementation includes data preprocessing, feature extraction, and model training on a nine‑category waste dataset. Performance is measured with precision, recall, and F1‑score to show how well the classifier works under different lighting and background conditions. This framework gives practical guidance

for building robust computer‑vision models for automated sorting plants. It helps move from manual waste separation to fast, intelligent recycling systems. The documentation also explains how the CASC algorithm improves feature representation, leading to high sorting accuracy for recyclable materials.

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