05 / CASE STUDY
MACHINE LEARNING / COMPUTER VISION · 2025
ADVERSEWEATHER
A local computer-vision experiment measuring vehicle detection under rain, fog, and snow with BDD100K and YOLOv5 transfer learning.
01 / THESIS
VISIBILITY
CHANGES
THE RESULT.
Adverse Weather AV Detection treats visibility as an evaluation condition: the same object-detection task is examined across rain, fog, and snow rather than presented as a production driving system.
The project keeps the result measurable and bounded. BDD100K supplies the dataset context, YOLOv5 supplies the detector, and recorded evaluation shows where performance becomes harder to interpret.
02 / THE EXPERIMENT
THE INPUT BECOMES HARDER TO SEE.
BDD100K frames move through weather conditions, YOLOv5 detection, and recorded evaluation without implying a production system.
03 / WEATHER FIELD
RAIN, FOG, AND SNOW REMAIN DISTINCT CONDITIONS.
04 / EVALUATION
MODEL LIMITS BECOME MEASURABLE.
Recorded car-class results keep the experiment legible: the numbers describe evaluation performance, not deployment readiness.
05 / DECISIONS
MEASURE THE DIFFICULT INPUT.
Weather as evaluation domain
Rain, fog, and snow remain explicit conditions so degraded visibility is part of the experiment rather than hidden behind one aggregate result.
CONDITION / DETECTION / EVALUATIONDetection before interpretation
YOLOv5 is evaluated on the car class before any broader claim about autonomous driving or operational deployment is made.
CONDITION / DETECTION / EVALUATIONMeasurement over anecdote
Precision, recall, mAP50, and mAP50-95 keep the experiment grounded in recorded model evaluation and visible limitations.
CONDITION / DETECTION / EVALUATION06 / SIGNATURE
VISIBILITY INTO UNCERTAINTY.
07 / DEMONSTRATES
ADVERSE CONDITIONS, OBJECT DETECTION, MEASURED PERFORMANCE.
BDD100K CONDITIONS
YOLOv5 DETECTION
RECORDED EVALUATION