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.

翻译：利用摄像头进行可靠的目标检测是自主驾驶车辆感知周围环境的关键。然而，现有面向自主驾驶的基于摄像头的目标检测方法缺乏对单帧检测性能提供全面反馈的能力。为解决这一局限，我们提出了一种名为检测质量指标（DQI）的新型评估度量，用于评估基于摄像头的目标检测算法性能，并提供逐帧的检测质量反馈。DQI通过融合细粒度显著性图的强度与目标检测算法的输出结果生成。此外，我们开发了一种基于超像素的注意力网络（SPA-NET），该网络以原始图像像素和超像素为输入，预测所提出的DQI评估度量。为验证该方法，我们在三个开源数据集上进行了实验。结果表明，所提出的评估度量能准确评估自主驾驶环境中基于摄像头的系统的检测质量。同时，所提出的SPA-NET优于其他流行的基于图像的质量回归模型，突显了DQI在评估摄像头感知视觉场景能力方面的有效性。总体而言，本工作为自主驾驶中基于摄像头的目标检测引入了有价值的自评估工具。