论文标题
探索针对无人机边缘部署的优化语义分割体系结构
Exploration of Optimized Semantic Segmentation Architectures for edge-Deployment on Drones
论文作者
论文摘要
在本文中，我们对网络参数对语义分割体系结构的影响进行分析。我们介绍了关于Dronedeploy分割基准测试的分析。基于比较分析，我们将基于Imagenet数据集的预审计编码器骨架确定最佳网络体系结构为FPN效率网络架构。在验证数据集上，网络的IOU得分为0.65，F1得分为0.71。我们还通过进一步探索了基于张力的优化的影响，从它们的内存足迹和推理潜伏期来比较各种体系结构。与模型相比，我们实现了〜4.1倍的内存节省，延迟提高了10％：FPN和骨干：InceptionResnetv2。
In this paper, we present an analysis on the impact of network parameters for semantic segmentation architectures in context of UAV data processing. We present the analysis on the DroneDeploy Segmentation benchmark. Based on the comparative analysis we identify the optimal network architecture to be FPN-EfficientNetB3 with pretrained encoder backbones based on Imagenet Dataset. The network achieves IoU score of 0.65 and F1-score of 0.71 over the validation dataset. We also compare the various architectures in terms of their memory footprint and inference latency with further exploration of the impact of TensorRT based optimizations. We achieve memory savings of ~4.1x and latency improvement of 10% compared to Model: FPN and Backbone: InceptionResnetV2.
