  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.
