A YOLOv8 segmentation and tracking system that detects, classifies, and counts rods and stems on conveyor lines in real time, without duplicate counts.
Manufacturing and processing facilities face challenges accurately detecting, classifying, and counting rods and stems moving on conveyor belts, where manual inspection is slow, inconsistent, and prone to error.
MoreYeahs built an AI-powered rod and stem detection and counting system for manufacturing organizations, giving production and quality control teams accurate, real-time counts without manual tallying.
Manual counting and legacy vision systems could not keep pace with high-speed, high-similarity production flow:
Slow, Error-Prone Manual Counting: manual inspection and counting were labor-intensive and inconsistent between operators.
Similar-Looking Objects: traditional sensor-based or rule-based vision systems struggled to distinguish similar-looking rods and stems.
Overlap and High-Speed Flow: partial overlap and continuous high-speed conveyor movement caused missed or duplicate counts.
Limited Production Visibility: lack of accurate real-time counts reduced visibility for quality control and throughput decisions.
MoreYeahs built an AI-powered vision system combining a custom-trained YOLOv8 segmentation model with ByteTrack multi-object tracking to detect, segment, classify, and count rods and stems in real time.
Pixel-Level Segmentation: YOLOv8 segmentation ensures precise object boundary detection for each rod and stem.
Persistent Object Tracking: ByteTrack assigns a persistent tracking ID to every detected object across frames.
Virtual Counting Line: a configurable line automatically increments class-wise counts when tracked objects cross it in the correct direction.
Operator Interface: a Streamlit-based web interface lets operators upload video, tune detection parameters, set the counting line, and export annotated video and count statistics.
The pipeline runs from video ingestion through segmentation, tracking, and counting, with safeguards against duplicate counts.
Segmentation and Classification: YOLOv8 generates pixel masks and class labels for each rod and stem in the frame.
Trajectory Maintenance: ByteTrack maintains object trajectories across frames, with tracking ID history preventing recounts.
Color-Coded Visualization: color-coded overlays distinguish rods and stems, with a dashboard showing real-time class-wise counts and totals.
Resilient Handling: overlapping or clustered objects are tracked as a group until separation, and temporarily lost detections retain their ID if re-detected within a buffer window.
The solution combines an industrial conveyor camera setup with a custom segmentation and tracking stack.
In validation testing, the system met its target benchmarks for detection, counting, and tracking accuracy.
Accurate, automated counting gives manufacturing teams real-time production visibility without manual tallying.
Reliable Production Counts: near-zero duplicate counting gives quality and operations teams a trustworthy throughput number.
Reduced Manual Labor: automated counting frees staff from repetitive manual tallying.
Operator Self-Service: the tunable interface lets line operators adjust detection parameters and counting rules without engineering support.
Extensible to Other Lines: the segmentation-and-tracking approach can extend to other similar-object counting use cases in the plant.
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