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Optimized Segmentation Model for Garbage Detection

We built an optimized segmentation system to detect and classify different types of waste in real time.
By enhancing model speed and accuracy through hybrid CPU-GPU inference, the solution enables faster sorting, smarter recycling, and sustainable automation across waste-management facilities.

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Summary :

We enhanced a large image-segmentation model to achieve real-time garbage detection and classification for waste-management systems.
Through model optimization and hybrid inference, the solution delivers faster, more accurate sorting that supports sustainability at scale.

Problem

Problem

Objectives

  • Boost segmentation speed while preserving accuracy.
  • Enable real-time inference for automated waste-sorting machines.
  • Balance GPU and CPU loads for efficiency.
  • Reduce overall processing cost and power use.

Challenges

  • Complex model layers causing inference delays.
  • Maintaining accuracy after optimization.
  • Managing resource utilization for hybrid hardware.
  • Handling cluttered backgrounds and overlapping objects.

Solution

We analyzed the existing architecture with ONNX and Netron to identify bottlenecks and rebuilt key layers with TensorRT for faster execution.
 A hybrid CPU-GPU inference pipeline was implemented to maximize throughput and minimize delay.
 The optimized model now detects and segments waste types instantly, enabling smarter recycling and waste-management workflows.

Architecture

Input Feed
Images or video streams of waste are passed to the model.
Model Optimization
ONNX and TensorRT streamline layers to cut inference time.
Hybrid Inference
CPU and GPU share processing for high-speed efficiency.
Segmentation Output
Objects are classified and segmented in real time.
Performance Analytics
System continuously measures speed and precision for tuning.

Results & Impact

Real-Time Processing Inference time reduced by over 40%, enabling live classification.

Higher Accuracy Improved precision for cluttered visuals and overlapping waste.

Efficient Hardware Use Balanced CPU-GPU utilization lowered computational cost.

Sustainable Automation Enabled smarter, faster, and greener waste-management systems.

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