High-altitude remote sensing target detection has problems related to its low precision and low detection rate. In order to enhance the performance of detecting remote sensing targets, a new YOLO (You Only Look Once)-V3-based algorithm was proposed. In our improved YOLO-V3, we introduced the concept of multi-receptive fields to enhance the performance of feature extraction. Therefore, the proposed model was termed Multi-Receptive Fields Fusion YOLO (MRFF-YOLO). In addition, to address the flaws of YOLO-V3 in detecting small targets, we increased the detection layers from three to four. Moreover, in order to avoid gradient fading, the structure of improved DenseNet was chosen in the detection layers. We compared our approach (MRFF-YOLO) with YOLO-V3 and other state-of-the-art target detection algorithms on an Remote Sensing Object Detection (RSOD) dataset and a dataset of Object Detection in Aerial Images (UCS-AOD). With a series of improvements, the mAP (mean average precision) of MRFF-YOLO increased from 77.10% to 88.33% in the RSOD dataset and increased from 75.67% to 90.76% in the UCS-AOD dataset. The leaking detection rates are also greatly reduced, especially for small targets. The experimental results showed that our approach achieved better performance than traditional YOLO-V3 and other state-of-the-art models for remote sensing target detection.

中文翻译：

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MRFF-YOLO：用于遥感目标检测的多接收场融合网络

高空遥感目标检测存在精度低，检测率低的问题。为了提高遥感目标的检测性能，提出了一种新的基于YOLO（仅看一次）-V3的算法。在改进的YOLO-V3中，我们引入了多接收场的概念来增强特征提取的性能。因此，提出的模型被称为多感受野融合YOLO（MRFF-YOLO）。此外，为了解决YOLO-V3在检测小目标方面的缺陷，我们将检测层从三层增加到了四层。此外，为了避免梯度衰落，在检测层中选择了改进的DenseNet结构。我们在遥感物体检测（RSOD）数据集和航空影像中物体检测（UCS-AOD）数据集上将我们的方法（MRFF-YOLO）与YOLO-V3和其他最新目标检测算法进行了比较。经过一系列改进，MRFF-YOLO的mAP（平均平均精度）在RSOD数据集中从77.10％增加到88.33％，在UCS-AOD数据集中从75.67％增加到90.76％。泄漏检测率也大大降低，特别是对于小目标。实验结果表明，我们的方法比传统的YOLO-V3和其他先进的遥感目标检测模型具有更好的性能。在RSOD数据集中占33％，在UCS-AOD数据集中从75.67％增加到90.76％。泄漏检测率也大大降低，特别是对于小目标。实验结果表明，我们的方法比传统的YOLO-V3和其他先进的遥感目标检测模型具有更好的性能。在RSOD数据集中占33％，在UCS-AOD数据集中从75.67％增加到90.76％。泄漏检测率也大大降低，特别是对于小目标。实验结果表明，我们的方法比传统的YOLO-V3和其他先进的遥感目标检测模型具有更好的性能。