论文标题
无人驾驶车辆中野外的车载物体检测
In-Vehicle Object Detection in the Wild for Driverless Vehicles
论文作者
论文摘要
车载人类物体识别在基于视觉的自动驾驶系统中起着重要作用，而道路或街道上的行人和车辆等物体是保护无人驾驶车辆的主要目标。一个挑战是在野生条件下检测物体在移动时的困难，而照明和图像质量可能会发生巨大变化。在这项工作中，为了应对这一挑战，我们利用了带有单射击检测器（SSD）的深卷积生成对抗网络（DCGAN）来处理野生条件。在我们的工作中，gan接受了低质量图像的培训，以应对智能城市的野生状况所带来的挑战，而级联的SSD则被用作与GAN一起执行的对象检测器。我们使用伦敦街上的出租车驾驶员视频在日光和夜间时间使用了在野外条件下测试的方法，而车载视频的测试表明，在野外条件下，这种策略可以极大地达到更好的检测率。
In-vehicle human object identification plays an important role in vision-based automated vehicle driving systems while objects such as pedestrians and vehicles on roads or streets are the primary targets to protect from driverless vehicles. A challenge is the difficulty to detect objects in moving under the wild conditions, while illumination and image quality could drastically vary. In this work, to address this challenge, we exploit Deep Convolutional Generative Adversarial Networks (DCGANs) with Single Shot Detector (SSD) to handle with the wild conditions. In our work, a GAN was trained with low-quality images to handle with the challenges arising from the wild conditions in smart cities, while a cascaded SSD is employed as the object detector to perform with the GAN. We used tested our approach under wild conditions using taxi driver videos on London street in both daylight and night times, and the tests from in-vehicle videos demonstrate that this strategy can drastically achieve a better detection rate under the wild conditions.
