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Improved YOLO-V3 with DenseNet for Multi-Scale Remote Sensing Target Detection

机译：改进的YOLO-V3与DENSENET用于多尺度遥感目标检测

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Remote sensing targets have different dimensions, and they have the characteristics of dense distribution and a complex background. This makes remote sensing target detection difficult. With the aim at detecting remote sensing targets at different scales, a new You Only Look Once (YOLO)-V3-based model was proposed. YOLO-V3 is a new version of YOLO. Aiming at the defect of poor performance of YOLO-V3 in detecting remote sensing targets, we adopted DenseNet (Densely Connected Network) to enhance feature extraction capability. Moreover, the detection scales were increased to four based on the original YOLO-V3. The experiment on RSOD (Remote Sensing Object Detection) dataset and UCS-AOD (Dataset of Object Detection in Aerial Images) dataset showed that our approach performed better than Faster-RCNN, SSD (Single Shot Multibox Detector), YOLO-V3, and YOLO-V3 tiny in terms of accuracy. Compared with original YOLO-V3, the mAP (mean Average Precision) of our approach increased from 77.10% to 88.73% in the RSOD dataset. In particular, the mAP of detecting targets like aircrafts, which are mainly made up of small targets increased by 12.12%. In addition, the detection speed was not significantly reduced. Generally speaking, our approach achieved higher accuracy and gave considerations to real-time performance simultaneously for remote sensing target detection.
机译：遥感目标具有不同的尺寸，它们具有密集的分布和复杂背景的特点。这使得遥感目标检测困难。旨在检测不同尺度的遥感目标，提出了一种新的您只有一次（YOLO）-V3的模型。 YOLO-V3是YOLO的新版本。针对YOLO-V3在检测遥感目标方面表现不佳的缺陷，我们采用了DENSENET（密集连接的网络）来提高特征提取能力。此外，基于原始YOLO-V3，检测尺度增加到四个。 RSOD的实验（遥感对象检测）数据集和UCS-AOD（在航空图像中的对象检测数据集）数据集显示我们的方法比Faster-RCNN，SSD（单次Multibox探测器），YOLO-V3和YOLO更好-v3在准确性方面微小。与原始YOLO-V3相比，我们的方法的地图（平均平均精度）从RSOD数据集中的77.10％增加到88.73％。特别是，检测飞机等目标的地图，主要由小目标组成增加12.12％。此外，检测速度没有显着降低。一般来说，我们的方法实现了更高的准确性，并同时对实时性能进行了考虑，以便遥感目标检测。

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