MRFF-YOLO: A Multi-Receptive Fields Fusion Network for Remote Sensing Target Detection

Danqing Xu,Yiquan Wu

doi:10.3390/rs12193118

Abstract

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.

Highlights

The high-altitude remote sensing images [1,2,3,4] obtained by satellites and aircrafts are widely used in military, navigation, disaster relief, etc
With ‘Dense block 1’ to ‘Dense block 4’ added in the detection layers, the mean average precision (mAP) improved from 87.49% to 88.33% and the FPS improved from 23.5 to 25.1, which indicated that the Dense blocks we proposed in the detection layers can modestly improve the accuracy of detecting remote sensing targets and accelerate the velocity of detection simultaneously
Aimed at the characteristics of remote sensing targets for which a large number of small targets exist in remote sensing images and their distribution is relatively dense, a series of improvements were proposed based on YOLO-V3

Summary

Introduction

The high-altitude remote sensing images [1,2,3,4] obtained by satellites and aircrafts are widely used in military, navigation, disaster relief, etc. The algorithm of elliptical Laplace operator filtering based on Gaussian scale space was proposed in 2010 [8] It treated the vehicle targets as elliptical class objects and employed elliptic operators in different directions to perform convolution filtering with the targets. In 2015, a new method for remote sensing target detection was proposed by Naoto Yokoya et al [9]. It combined feature detection based on sparse representation with generalized Huff transform. By adopting the method of learning the dictionary of targets and backgrounds, the sparse image representation of specific classes was constantly supplemented. When facing the remote sensing targets under complex background, these conventional algorithms still had some problems such as low detection accuracy, error detections, and missed detections

Methods

Results

Conclusion