Robustness in Compressed Neural Networks for Object Detection

TL;DR

This paper investigates how model compression affects the robustness of neural networks for object detection, especially in autonomous driving, highlighting nuanced impacts of different corruptions and the benefits of data augmentation and imbalance handling.

Contribution

It provides a detailed analysis of robustness issues in compressed models for object detection and evaluates strategies like data augmentation and imbalance handling to improve safety-critical performance.

Findings

Sensitivity to corruptions varies with compression type.

Data augmentation improves robustness of compressed models.

Handling data imbalance enhances worst-class detection accuracy.

Abstract

Model compression techniques allow to significantly reduce the computational cost associated with data processing by deep neural networks with only a minor decrease in average accuracy. Simultaneously, reducing the model size may have a large effect on noisy cases or objects belonging to less frequent classes. It is a crucial problem from the perspective of the models' safety, especially for object detection in the autonomous driving setting, which is considered in this work. It was shown in the paper that the sensitivity of compressed models to different distortion types is nuanced, and some of the corruptions are heavily impacted by the compression methods (i.e., additive noise), while others (blur effect) are only slightly affected. A common way to improve the robustness of models is to use data augmentation, which was confirmed to positively affect models' robustness, also for highly compressed models. It was further shown that while data imbalance methods brought only a slight increase in accuracy for the baseline model (without compression), the impact was more striking at higher compression rates for the structured pruning. Finally, methods for handling data imbalance brought a significant improvement of the pruned models' worst-detected class accuracy.