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

• URL: http://arxiv.org/abs/2203.02588v2
• Date: Thu, 1 Jun 2023 01:45:39 GMT
• Title: A Quality Index Metric and Method for Online Self-Assessment of Autonomous Vehicles Sensory Perception
• Authors: Ce Zhang and Azim Eskandarian
• Abstract summary: We propose a novel evaluation metric, named as the detection quality index (DQI), which assesses the performance of camera-based object detection algorithms. 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.
• Score: 164.93739293097605
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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