Examining the Impact of Optical Aberrations to Image Classification and Object Detection Models
This work addresses the robustness of vision models in safety-critical applications by highlighting gaps in existing blur corruption benchmarks, though it is incremental as it focuses on dataset creation rather than new methods.
The paper tackled the problem of evaluating deep neural network robustness to realistic optical blur effects by proposing two datasets, OpticsBench and LensCorruptions, and found that performance varied significantly across pre-trained models on ImageNet and MSCOCO, indicating a need for more realistic corruption benchmarks.
Deep neural networks (DNNs) have proven to be successful in various computer vision applications such that models even infer in safety-critical situations. Therefore, vision models have to behave in a robust way to disturbances such as noise or blur. While seminal benchmarks exist to evaluate model robustness to diverse corruptions, blur is often approximated in an overly simplistic way to model defocus, while ignoring the different blur kernel shapes that result from optical systems. To study model robustness against realistic optical blur effects, this paper proposes two datasets of blur corruptions, which we denote OpticsBench and LensCorruptions. OpticsBench examines primary aberrations such as coma, defocus, and astigmatism, i.e. aberrations that can be represented by varying a single parameter of Zernike polynomials. To go beyond the principled but synthetic setting of primary aberrations, LensCorruptions samples linear combinations in the vector space spanned by Zernike polynomials, corresponding to 100 real lenses. Evaluations for image classification and object detection on ImageNet and MSCOCO show that for a variety of different pre-trained models, the performance on OpticsBench and LensCorruptions varies significantly, indicating the need to consider realistic image corruptions to evaluate a model's robustness against blur.