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Benchmark Pallet Defect Detection

This guide demonstrates how to benchmark the pallet defect detection pipeline to determine optimal stream density and performance characteristics.

Contents

Prerequisites

Ensure the application is set up and running. Refer to the Get Started Guide for complete installation and configuration steps.

  • DL Streamer Pipeline Server (DLSPS) running and accessible
  • curl, jq, gawk, ffmpeg, and bc utilities installed

Benchmark Script Usage

Navigate to the [WORKDIR]/edge-ai-suites/manufacturing-ai-suite/industrial-edge-insights-vision directory and use the benchmark script:

./benchmark_start.sh -p <pipeline_name> -l <lower_bound> -u <upper_bound> [-t <target_fps>] [-i <interval>]

Arguments:

  • -p <pipeline_name> : (Required) The name of the pipeline to benchmark (e.g., pallet_defect_detection)
  • -l <lower_bound> : (Required) Starting lower bound for number of streams
  • -u <upper_bound> : (Required) Starting upper bound for number of streams
  • -t <target_fps> : Target FPS threshold (default: 14.95)
  • -i <interval> : Monitoring duration in seconds per test run (default: 60)

Configuration

The benchmark script automatically uses the configured sample application and its payload file:

  1. Application Selection: The script reads SAMPLE_APP from the .env file to determine which application to benchmark
  2. Payload Configuration: Uses the standard payload.json file from the selected application directory (apps/${SAMPLE_APP}/payload.json)
  3. Pipeline Selection: Choose from available pipelines in the payload file (e.g., pallet_defect_detection, pallet_defect_detection_gpu)

Available pipelines for pallet defect detection:

  • pallet_defect_detection: CPU-based detection pipeline
  • pallet_defect_detection_gpu: GPU-accelerated detection pipeline with optimized settings

Recommended Pipeline Parameters

These are the recommended parameters by Edge Benchmarking and Workloads team for workload with similar characteristics. These are configurable parameters that can be adjusted based on your specific requirements:

inference-region=full-frame inference-interval=1 batch-size=8 nireq=2 ie-config="GPU_THROUGHPUT_STREAMS=2" threshold=0.7

Parameter Descriptions:

  • inference-region=full-frame: Process the entire frame for detection
  • inference-interval=1: Run inference on every frame
  • batch-size=8: Process 8 frames in a single batch for better GPU utilization
  • nireq=2: Number of inference requests to run in parallel
  • ie-config="GPU_THROUGHPUT_STREAMS=2": Intel OpenVINO engine streams configuration
  • threshold=0.7: Detection confidence threshold (70%)

Steps to run benchmarks

  1. Set up the environment: Ensure SAMPLE_APP=pallet-defect-detection is set in your .env file

  2. Test CPU performance:

    ./benchmark_start.sh -p pallet_defect_detection -l 1 -u 10 -t 25.0 -i 30

  3. Test GPU performance (if available):

    ./benchmark_start.sh -p pallet_defect_detection_gpu -l 1 -u 20 -t 28.5 -i 60

    NOTE: The script automatically uses the payload.json file from the configured sample application directory.

Understanding Results

The benchmark uses binary search to find optimal stream density. Key metrics include:

  • Stream Density: Maximum concurrent streams achieving target FPS
  • Throughput Median: Median FPS across all streams
  • Throughput Cumulative: Total FPS sum of all streams

Sample Output:

streams: 6 throughput: 28.8 range: [5,7]
======================================================
✅ FINAL RESULT: Stream-Density Benchmark Completed!
stream density: 6
======================================================
throughput median: 29.0
throughput cumulative: 173.8

Troubleshooting

Common Issues:

  1. DL Streamer Pipeline Server Not Accessible

    Error: DL Streamer Pipeline Server is not running or not reachable
    • Verify DL Streamer Pipeline Server is running: docker ps | grep dlstreamer
    • Check network connectivity to localhost

  2. Pipeline Startup Failures

    • Check model file paths in payload.json
    • Verify video file accessibility
    • Monitor system resources (CPU, memory, GPU)
    • Ensure the correct SAMPLE_APP is set in .env file

  3. Pipeline Not Found

    Error: Pipeline 'pipeline_name' not found in payload.json
    • Verify the pipeline name exists in apps/${SAMPLE_APP}/payload.json
    • Check available pipelines: jq '.[].pipeline' apps/${SAMPLE_APP}/payload.json

  4. Debug Mode Add --trace to see detailed execution steps:

    ./benchmark_start.sh -p pallet_defect_detection_gpu -l 1 -u 10 --trace