Dynamic Monitoring System for the Feed Inlet of Waste-to-Energy Plants Based on an Improved Lightweight Model

Under the dual-carbon strategic framework, this paper proposes an intelligent monitoring system based on improved YOLOv8-seg for Waste-to-Energy plant feed inlets, addressing critical challenges in operational efficiency and automation. This system improves the core feature extraction module of the YOLOv8-seg model using the Dualconv module, proposes a new lightweight C2f_Dual module, and optimizes the overall architecture of YOLOv8-seg, which allows the proposed new model to achieve an 8.5% reduction in computational complexity (GFLOPs) while maintaining 97.8% original segmentation accuracy. Meanwhile, to cope with the demands of real industrial scenarios, a prediction module is designed in this paper, so that this monitoring system can dynamically predict the state of waste level at the feeding port in real time. Real-world industrial deployments have proven that this method can reduce labor costs and drive automation in Waste-to-Energy plants.