Exploration of Optimized Semantic Segmentation Architectures for edge-Deployment on Drones

• URL: http://arxiv.org/abs/2007.02839v1
• Date: Mon, 6 Jul 2020 15:49:18 GMT
• Title: Exploration of Optimized Semantic Segmentation Architectures for edge-Deployment on Drones
• Abstract summary: We present an analysis on the impact of network parameters for semantic segmentation architectures in context of UAV data processing. We identify the optimal network architecture to be FPN-EfficientNetB3 with pretrained backbone encoders based on Imagenet. We achieve memory savings of 4.1x and latency improvement of 10% compared to Model: FPN and Backbone: InResnetV2.
• Score: 5.349223987137843
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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