Quantization Robustness to Input Degradations for Object Detection
Authors: name: Toghrul Karimov, name: Hassan Imani, name: Allan Kazakov

Post-training quantization (PTQ) is crucial for deploying efficient object
detection models, like YOLO, on resource-constrained devices. However, the
impact of reduced precision on model robustness to real-world input
degradations such as noise, blur, and compression artifacts is a significant
concern. This paper presents a comprehensive empirical study evaluating the
robustness of YOLO models (nano to extra-large scales) across multiple
precision formats: FP32, FP16 (TensorRT), Dynamic UINT8 (ONNX), and Static INT8
(TensorRT). We introduce and evaluate a degradation-aware calibration strategy
for Static INT8 PTQ, where the TensorRT calibration process is exposed to a mix
of clean and synthetically degraded images. Models were benchmarked on the COCO
dataset under seven distinct degradation conditions (including various types
and levels of noise, blur, low contrast, and JPEG compression) and a
mixed-degradation scenario. Results indicate that while Static INT8 TensorRT
engines offer substantial speedups (~1.5-3.3x) with a moderate accuracy drop
(~3-7% mAP50-95) on clean data, the proposed degradation-aware calibration did
not yield consistent, broad improvements in robustness over standard clean-data
calibration across most models and degradations. A notable exception was
observed for larger model scales under specific noise conditions, suggesting
model capacity may influence the efficacy of this calibration approach. These
findings highlight the challenges in enhancing PTQ robustness and provide
insights for deploying quantized detectors in uncontrolled environments. All
code and evaluation tables are available at https://github.com/AllanK24/QRID.