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Status: Unchecked ×Keyword: Inception V2 ×Clear all

3 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   Human Detection in Aerial Thermal Images Using Faster R-CNN and SSD Algorithms

   Akshatha, Karunakar, Shenoy, Pai, Nagaraj and Rohatgi · Electronics · 2022

   The study compared Faster R-CNN and SSD detectors with different backbone networks for finding humans in aerial thermal images, measuring accuracy and speed on two standard datasets.

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   1. UncheckedFaster R-CNN with a ResNet50 backbone was the most accurate detector, scoring 100% mAP on the OSU thermal dataset and 55.7% on AAU PD T.“The evaluation results demonstrate that the Faster R-CNN model trained with the ResNet50 network architecture out-performed in terms of detection accuracy, with a mean average precision (mAP at 0.5 IoU) of 100% and 55.7% for the test data of the OSU thermal dataset and AAU PD T datasets, respective…”
   2. UncheckedIn this study, SSD with a MobileNet-v1 backbone was the fastest detector tested, running at 44 frames per second on an NVIDIA GTX 1080 GPU.“SSD with MobileNet-v1 achieved the highest detection speed of 44 frames per second (FPS) on the NVIDIA GeForce GTX 1080 GPU.”
   3. UncheckedTuning anchor parameters raised mAP by 10% for Faster R-CNN ResNet50 and 3.5% for SSD Inception-v2 on the hard AAU PD T aerial thermal dataset.“Fine-tuning the anchor parameters of the Faster R-CNN ResNet50 and SSD Inception-v2 algorithms caused remarkable improvement in mAP by 10% and 3.5%, respectively, for the challenging AAU PD T dataset.”