Object Detection: Evaluation & Safety Design
Explore how to evaluate and ensure safety in object detection systems for autonomous vehicles. Understand metric design including mAP, IoU thresholds, and false negative rates. Discover how fail-safe mechanisms with a three-zone confidence framework handle uncertain detections. Learn to apply slice-based evaluation to uncover hidden failures across rare classes and adverse conditions. Understand regulatory requirements such as ISO 26262 and the EU AI Act that govern safety-critical ML systems.
We'll cover the following...
A compressed, calibrated object detection model sits on the edge device, ready to process camera frames at highway speed. But before a single inference runs in production, one question demands an answer: how do you know this model is safe enough to trust with human lives? Standard ML evaluation, the kind that reports a single accuracy number on a held-out test set, cannot answer that question. Aggregate metrics hide the failures that kill people.
Consider a concrete interview scenario. You are designing the evaluation pipeline for an autonomous vehicle’s pedestrian detection system. A missed pedestrian (a false negative) results in a collision. A phantom detection (a false positive) triggers unnecessary braking, which is annoying but survivable. These two failure modes are not symmetric. Evaluation must reflect this asymmetry explicitly, or the system ships with blind spots that only surface after an incident.
This lesson covers three integrated evaluation and safety pillars that address this challenge. First, metric design using mAP, IoU thresholds, and false negative rate. Second, fail-safe system behavior when detection confidence is low. Third, slice-based evaluation across adverse conditions that aggregate numbers conceal. Regulatory compliance ties these pillars together into a system that is not only performant but deployable under real-world legal constraints.
mAP, IoU thresholds, and false negative rate
Every object detection evaluation begins with a spatial question: how well does the predicted bounding box overlap with the ground-truth box?
Intersection over union and threshold selection
Intersection over Union (IoU) is the ratio of the overlapping area between a predicted bounding box and a ground-truth bounding box to their combined union area, producing a value between 0 (no overlap) and 1 (perfect overlap). This single number determines whether a detection counts as correct. Setting the IoU threshold at 0.5 (the COCO-style mAP@0.5 standard) is lenient and accepts coarse localization. Raising it to 0.75 (mAP@0.75) demands tight bounding box alignment. The COCO primary metric, mAP@[0.5:0.95], averages performance across ten IoU thresholds from 0.5 to 0.95 in steps of 0.05, providing a comprehensive view of localization quality.
For each object class, the system computes a precision-recall curve by sweeping the confidence threshold. Average Precision (AP) is the area under the precision-recall curve for a single object class, summarizing how well the model ranks correct detections above incorrect ones across all confidence levels. Mean Average Precision (mAP) is simply the mean of AP ...