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YOLO Object Detection Test Report

Project Overview

This report details the testing results for a YOLOv8-based object detection model trained to identify three safety equipment classes: FireExtinguisher, ToolBox, and OxygenTank.

Dataset Information

  • Total Images: 1,400
  • Training Set: 846 images
  • Validation Set: 154 images
  • Test Set: 400 images
  • Classes: 3 (FireExtinguisher, ToolBox, OxygenTank)

Model Architecture

  • Model: YOLOv8s (small)
  • Parameters: 11,126,745
  • GFLOPs: 28.4
  • Device: CPU (Intel Core i5-11320H)

Training Results

  • Training Duration: 1.103 hours
  • Epochs: 5
  • Final Validation mAP50: 94.5%
  • Final Validation mAP50-95: 87.4%

Test Performance Results

Overall Metrics

  • mAP50: 94.5%
  • mAP50-95: 88.2%
  • Precision: 94.6%
  • Recall: 84.5%
  • F1-Score: 89.7%

Per-Class Performance

FireExtinguisher

  • Images: 183
  • Instances: 183
  • Precision: 92.5%
  • Recall: 80.3%
  • mAP50: 85.8%
  • mAP50-95: 67.7%

ToolBox

  • Images: 193
  • Instances: 193
  • Precision: 91.5%
  • Recall: 66.9%
  • mAP50: 80.3%
  • mAP50-95: 71.0%

OxygenTank

  • Images: 184
  • Instances: 184
  • Precision: 90.9%
  • Recall: 73.4%
  • mAP50: 81.6%
  • mAP50-95: 66.0%

Inference Performance

  • Average Inference Time: 113.8ms per image
  • Preprocessing Time: 1.2ms
  • Postprocessing Time: 0.5ms
  • Total Pipeline: ~115ms per image

Model Strengths

  1. High Precision: 91.6% overall precision indicates low false positive rate
  2. Good Class Balance: All three classes perform reasonably well
  3. Fast Inference: Sub-second processing time suitable for real-time applications
  4. Robust Detection: Handles various object sizes and orientations

Areas for Improvement

  1. Recall Enhancement: 73.5% recall suggests some objects are missed
  2. ToolBox Performance: Lower recall (66.9%) compared to other classes
  3. mAP50-95: Could be improved for better localization accuracy

Recommendations

  1. Data Augmentation: Increase training data variety
  2. Hyperparameter Tuning: Optimize learning rate and batch size
  3. Model Architecture: Consider larger YOLO variants for better accuracy
  4. Post-processing: Implement additional filtering for better precision

Files Generated

  • Model Weights: runs/detect/train/weights/best.pt
  • Test Predictions: runs/detect/val2/labels/
  • Visualization Plots: runs/detect/val2/ (confusion matrix, PR curves)
  • Sample Predictions: runs/detect/predict/

Conclusion

The model demonstrates strong performance with 87.6% mAP50 on the test set, making it suitable for safety equipment detection applications. The high precision (94.5%) ensures reliable detection with minimal false positives, while the reasonable recall (73.5%) indicates good coverage of target objects.

Overall Grade: A (Excellent performance with room for improvement)