# A Quality Index Metric and Method for Online Self-Assessment of Autonomous Vehicles Sensory Perception

arXiv: https://arxiv.org/abs/2203.02588 · HTML: https://mlsanity.com/paper/2203.02588/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 58 references that are in the map.
- First posted on arXiv 4 Mar 2022.

## Builds on (most cited first, 28 of 28)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning](https://mlsanity.com/paper/1805.04687/) (2018)
- [MUSIQ: Multi-scale Image Quality Transformer](https://mlsanity.com/paper/2108.05997/) (2021)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection](https://mlsanity.com/paper/1912.13192/) (2019)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network](https://mlsanity.com/paper/1907.02665/) (2019)
- [MetaIQA: Deep Meta-learning for No-Reference Image Quality Assessment](https://mlsanity.com/paper/2004.05508/) (2020)
- [DSSD : Deconvolutional Single Shot Detector](https://mlsanity.com/paper/1701.06659/) (2017)
- [Probabilistic two-stage detection](https://mlsanity.com/paper/2103.07461/) (2021)
- [YOLOP: You Only Look Once for Panoptic Driving Perception](https://mlsanity.com/paper/2108.11250/) (2021)
- [Transformer for Image Quality Assessment](https://mlsanity.com/paper/2101.01097/) (2020)
- [Stable, Fast and Accurate: Kernelized Attention with Relative Positional Encoding](https://mlsanity.com/paper/2106.12566/) (2021)
- [Adaptive Fractional Dilated Convolution Network for Image Aesthetics Assessment](https://mlsanity.com/paper/2004.03015/) (2020)
- [Attention-based Neural Network for Driving Environment Complexity Perception](https://mlsanity.com/paper/2106.11277/) (2021)
- [Semi-Supervised Deep Ensembles for Blind Image Quality Assessment](https://mlsanity.com/paper/2106.14008/) (2021)
- [A survey on IQA](https://mlsanity.com/paper/2109.00347/) (2021)
- [A Survey and Tutorial of EEG-Based Brain Monitoring for Driver State Analysis](https://mlsanity.com/paper/2008.11226/) (2020)

## Built on by (most cited first, 10 of 10)

- [MotionTrack: End-to-End Transformer-based Multi-Object Tracing with LiDAR-Camera Fusion](https://mlsanity.com/paper/2306.17000/) (2023)
- [Uncertainty estimation of pedestrian future trajectory using Bayesian approximation](https://mlsanity.com/paper/2205.01887/) (2022)
- [SalienDet: A Saliency-based Feature Enhancement Algorithm for Object Detection for Autonomous Driving](https://mlsanity.com/paper/2305.06940/) (2023)
- [A Survey of Document-Level Information Extraction](https://mlsanity.com/paper/2309.13249/) (2023)
- [Analyzing and Mitigating Bias for Vulnerable Classes: Towards Balanced Representation in Dataset](https://mlsanity.com/paper/2401.10397/) (2024)
- [Image-Guided Outdoor LiDAR Perception Quality Assessment for Autonomous Driving](https://mlsanity.com/paper/2406.17265/) (2024)
- [Synthetic Dataset Evaluation Based on Generalized Cross Validation](https://mlsanity.com/paper/2509.11273/) (2025)
- [A Style-Based Profiling Framework for Quantifying the Synthetic-to-Real Gap in Autonomous Driving Datasets](https://mlsanity.com/paper/2510.10203/) (2025)
- [SEGA: A Transferable Signed Ensemble Gaussian Black-Box Attack against No-Reference Image Quality Assessment Models](https://mlsanity.com/paper/2509.18546/) (2025)
- [The Impact of Different Backbone Architecture on Autonomous Vehicle Dataset](https://mlsanity.com/paper/2309.08564/) (2023)

## Abstract (arXiv)

Reliable object detection using cameras plays a crucial role in enabling autonomous vehicles to perceive their surroundings. However, existing camera-based object detection approaches for autonomous driving lack the ability to provide comprehensive feedback on detection performance for individual frames. To address this limitation, we propose a novel evaluation metric, named as the detection quality index (DQI), which assesses the performance of camera-based object detection algorithms and provides frame-by-frame feedback on detection quality. The DQI is generated by combining the intensity of the fine-grained saliency map with the output results of the object detection algorithm. Additionally, we have developed a superpixel-based attention network (SPA-NET) that utilizes raw image pixels and superpixels as input to predict the proposed DQI evaluation metric. To validate our approach, we conducted experiments on three open-source datasets. The results demonstrate that the proposed evaluation metric accurately assesses the detection quality of camera-based systems in autonomous driving environments. Furthermore, the proposed SPA-NET outperforms other popular image-based quality regression models. This highlights the effectiveness of the DQI in evaluating a camera's ability to perceive visual scenes. Overall, our work introduces a valuable self-evaluation tool for camera-based object detection in autonomous vehicles.