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2 claims from 1 paper

1. Computer Science › Advanced Neural Network Applications

   FFCA-YOLO for Small Object Detection in Remote Sensing Images

   Zhang, Ye, Zhu, Liu, Guo and Yan · IEEE Transactions on Geoscience and Remote Sensing · 2024

   The paper proposes FFCA-YOLO, a detector for small objects in remote sensing images, built from three lightweight modules, plus a lighter version, and tests both on three datasets.

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   1. UncheckedFFCA-YOLO scores mAP50 of 0.748, 0.617 and 0.909 on three small-object remote sensing datasets, which the paper says beats several benchmark and leading methods.“The accuracy of FFCA-YOLO reaches 0.748, 0.617 and 0.909(in terms of mAP50) that exceeds several benchmark models and state-of-the-art methods.”
   2. UncheckedA lighter version of FFCA-YOLO runs faster and needs less computing power and fewer parameters, with little loss of accuracy, the paper states.“L-FFCA-YOLO has faster speed, smaller parameter scale, lower computing power requirement but little accuracy loss compared with FFCA-YOLO.”