论文标题
评估嵌入式GPU平台上用于车辆辅助系统应用的热成像
Evaluation of Thermal Imaging on Embedded GPU Platforms for Application in Vehicular Assistance Systems
论文作者
论文摘要
这项研究的重点是通过在GPU和单板边缘GPU计算平台上部署训练有素的网络来评估智能和安全车辆系统的热对象检测的实时性能，以用于车载汽车传感器套件测试。在充满挑战的天气和环境场景中，采集，处理和开源的一个新型的大型热数据集，其中包括> 35,000个不同的帧。该数据集是从丢失的成本但有效的未冷却的LWIR热摄像机，安装的独立车和电动汽车上记录的，以最大程度地减少机械振动。最先进的Yolo-V5网络变体经过四个不同的公共数据集以及新获取的本地数据集培训，通过使用SGD Optimizer，以最佳的DNN概括。使用各种定量指标在广泛的测试数据上验证了训练的网络的有效性，包括精度，召回曲线，平均平均精度和每秒帧。使用Tensorrt推断加速器进一步优化了Yolo的较小网络变体，以明确提高每秒速率的帧。当在低功率边缘设备上进行测试时，优化的网络发动机将每秒帧的帧增加3.5倍，从而在Nvidia Jetson Nano上达到11 fps，而NVIDIA Xavier NX开发板上的NVIDIA Jetson Nano和60 fps。
This study is focused on evaluating the real-time performance of thermal object detection for smart and safe vehicular systems by deploying the trained networks on GPU & single-board EDGE-GPU computing platforms for onboard automotive sensor suite testing. A novel large-scale thermal dataset comprising of > 35,000 distinct frames is acquired, processed, and open-sourced in challenging weather and environmental scenarios. The dataset is a recorded from lost-cost yet effective uncooled LWIR thermal camera, mounted stand-alone and on an electric vehicle to minimize mechanical vibrations. State-of-the-art YOLO-V5 networks variants are trained using four different public datasets as well newly acquired local dataset for optimal generalization of DNN by employing SGD optimizer. The effectiveness of trained networks is validated on extensive test data using various quantitative metrics which include precision, recall curve, mean average precision, and frames per second. The smaller network variant of YOLO is further optimized using TensorRT inference accelerator to explicitly boost the frames per second rate. Optimized network engine increases the frames per second rate by 3.5 times when testing on low power edge devices thus achieving 11 fps on Nvidia Jetson Nano and 60 fps on Nvidia Xavier NX development boards.
