ML QA Computer Vision Assessment

Computer Vision
notebook
python
Author

Atila Madai

Published

June 22, 2025

✈️ Computer Vision QA Assessment for Airport Apron Annotation Pipelines

📘 Introduction

This notebook provides a robust framework for performing quality assurance (QA) on annotated image datasets used in computer vision (CV) pipelines for airport apron operations. These operations involve detecting and tracking aircraft, vehicles, ground crew, and equipment to ensure timely and safe turnaround processes. Reliable annotation is critical in such safety-critical environments, where every mislabeled object or faulty bounding box can undermine model accuracy and operational trust.

We focus on a synthetic dataset representing apron scenarios, using per-frame CSV annotations. The QA framework evaluates the correctness of bounding boxes, label consistency, and visual alignment before converting the data into the COCO format—a widely used standard for training object detection models.

🧠 Techniques Used

  • CSV Parsing (via pandas): Structured loading of annotation files
  • Bounding Box QA Checks: Ensure spatial validity and logical coherence
  • Visual Validation: Overlay annotations on images for human-in-the-loop inspection
  • COCO Format Conversion: Interoperable export for ML pipelines
  • Schema Revalidation: Final consistency checks after format transformation

🗂 Dataset Description

The dataset includes synthetic images of airport apron scenes and CSV annotation files with the following structure:

  • frame_id: Frame number or timestamp
  • object_id: Unique identifier per object
  • object_type: Class label (e.g., ‘aircraft’, ‘baggage_cart’, ‘person’)
  • x_min, y_min, x_max, y_max: Bounding box coordinates

📐 Pipeline Flow

flowchart TD
    A[Load Annotation CSVs] --> B[QA Sanity Checks]
    B --> C[Bounding Box Visual Overlay]
    C --> D[Convert to COCO Format]
    D --> E[Schema Re-validation]
    E --> F[Export QA-Ready Dataset]

🔬 Literature & Standards

  • Wang et al. (2022). Airport Apron Monitoring Using Deep Learning on Synthetic Imagery. DOI:10.3390/s22082983
  • Chen et al. (2023). Foreign Object Detection for Airport Runways Using YOLO. IEEE Access
  • Raza et al. (2021). Visual QA for Surveillance in Critical Infrastructure. arXiv:2105.11100

✅ Outcome

At the end of this notebook, we will have: - Validated bounding box annotations - Visual verification of object labeling - Exported a clean, COCO-compatible dataset

This workflow provides a reliable, repeatable QA process that can be adapted to other CV use cases in airside, industrial, or transportation domains.

🧪 ML QA & Monitoring for Real-Time Computer Vision

This notebook simulates quality assurance logic for real-time CV pipelines, focusing on monitoring inference confidence, detecting model drift, applying anomaly detection techniques, and tracking experiments using MLflow.

Code 
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import IsolationForest

📥 Load Inference Data

Code 
data = pd.read_csv('../../_data/CV/synthetic_cv_data.csv')
data['timestamp'] = pd.to_datetime(data['timestamp'])
data.head()
timestamp bbox_area confidence class
0 2025-01-01 00:00:00 2149.014246 0.960352 pushback
1 2025-01-01 00:00:07 1958.520710 0.906746 boarding
2 2025-01-01 00:00:14 2194.306561 0.787079 boarding
3 2025-01-01 00:00:21 2456.908957 0.802327 refuelling
4 2025-01-01 00:00:28 1929.753988 0.840601 boarding

📉 Confidence Score Monitoring

Visualize the rolling confidence mean to detect potential drift.

Code 
rolling_avg = data['confidence'].rolling(50).mean()

plt.figure(figsize=(12, 5))
plt.plot(data['timestamp'], data['confidence'], alpha=0.3, label='Confidence')
plt.plot(data['timestamp'], rolling_avg, color='red', label='Rolling Mean (50)')
plt.axhline(0.6, linestyle='--', color='gray', label='Alert Threshold')
plt.legend()
plt.title('Confidence Score Over Time')
plt.xlabel('Timestamp')
plt.ylabel('Confidence')
plt.show()

🚨 Alert Trigger

Code 
if rolling_avg.iloc[-1] < 0.6:
    print("⚠️ ALERT: Confidence degradation detected!")
else:
    print("✅ System performance is stable.")
⚠️ ALERT: Confidence degradation detected!

🤖 Anomaly Detection via Isolation Forest

Code 
features = data[['bbox_area', 'confidence']]
model = IsolationForest(contamination=0.05, random_state=42)
data['anomaly'] = model.fit_predict(features)
data['anomaly'] = data['anomaly'].map({1: 'Normal', -1: 'Anomaly'})
data['anomaly'].value_counts()
anomaly
Normal     475
Anomaly     25
Name: count, dtype: int64

🔍 Visualize Anomalies

Code 
plt.figure(figsize=(12, 5))
colors = {'Normal': 'blue', 'Anomaly': 'red'}
for label, group in data.groupby('anomaly'):
    plt.scatter(group['timestamp'], group['confidence'], c=colors[label], label=label, alpha=0.6)
plt.title('Detected Anomalies in Confidence')
plt.xlabel('Timestamp')
plt.ylabel('Confidence')
plt.legend()
plt.show()

📦 MLflow Logging

Log model parameters, performance metrics, and version metadata using MLflow.

Code 
import mlflow
import mlflow.sklearn

# Start an MLflow run
with mlflow.start_run(run_name="cv_qa_drift_monitoring"):
    mlflow.log_param("model_type", "IsolationForest")
    mlflow.log_param("contamination", 0.05)
    mlflow.log_metric("anomaly_count", (data['anomaly'] == "Anomaly").sum())
    mlflow.log_metric("mean_confidence", data['confidence'].mean())
    mlflow.log_metric("rolling_avg_last", rolling_avg.iloc[-1])
    mlflow.sklearn.log_model(model, "model")
    mlflow.set_tag("version", "v1.0")
    mlflow.set_tag("context", "QA pipeline drift detection")

print("✅ MLflow run logged.")
2025/06/22 20:25:38 WARNING mlflow.models.model: `artifact_path` is deprecated. Please use `name` instead.
2025/06/22 20:25:52 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.
✅ MLflow run logged.

🧩 Simulated kubectl Logs for ML System Monitoring

Real-time ML pipelines are often deployed in Kubernetes pods. QA engineers frequently inspect pod logs and system status using kubectl. Below is a simulated log parser that mimics reading pod health and anomaly alerts from a log file.

Code 
import time

# Simulated kubectl logs (mocked string output)
kubectl_logs = """
[2025-06-20 08:01:13] pod/vision-inferencer Ready
[2025-06-20 08:01:19] INFO: Inference pipeline started.
[2025-06-20 08:01:22] WARN: Confidence dropped below 0.65
[2025-06-20 08:01:25] ALERT: Detected anomaly cluster in frame stream 41
[2025-06-20 08:01:30] pod/vision-inferencer Healthy
"""

# Display line-by-line to mimic real-time stream
for line in kubectl_logs.strip().split('\n'):
    print(line)
    time.sleep(0.3)
[2025-06-20 08:01:13] pod/vision-inferencer Ready
[2025-06-20 08:01:19] INFO: Inference pipeline started.
[2025-06-20 08:01:22] WARN: Confidence dropped below 0.65
[2025-06-20 08:01:25] ALERT: Detected anomaly cluster in frame stream 41
[2025-06-20 08:01:30] pod/vision-inferencer Healthy

✅ Next Steps

  • Monitor system metrics via Prometheus/Grafana
  • Deploy QA checks in CI/CD with pytest
  • Integrate tracking with MLflow and experiment registry
  • Simulate larger datasets with DVC for long-term monitoring