Efficiency Analysis of Wrecking Waste Classification Using Neural Network
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2025
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This study examines the efficiency of a neural approach to identifying and classifying wrecking waste in photographic data. Building on a two-factor design that couples scene-level detection with per-class binary refinement, we quantify where accuracy is earned, where it is lost, and which components: architecture, data composition, augmentation, and arbitration – most strongly govern the outcome. Using a composite dataset of ten material classes, we report that the hybrid method attains up to 97.8% classification accuracy, with substantial gains over a detector-only baseline on heterogenous or texture-confusable categories. Detector metrics such as mAP50 ≈ 0.746 and mAP50–95 ≈ 0.669 confirm reliable localization, while binary residual classifiers close the gap in label assignment, lifting macro F1 from ≈0.751 to ≈0.974. These findings indicate that efficiency, measured as correct material routing per unit inference cost, is maximized when detection and classification are decoupled yet reconciled through a calibrated arbitration rule.
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Zalutska O., Mazurets O., Molchanova M. Efficiency Analysis of Wrecking Waste Classification Using Neural Network. Information Technology and Implementation (Satellite). Proceedings 12th International Conference. November 21, 2025. Kyiv, Ukraine. 2025. Pp. 142-143.