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

Xu, Danqing; Wu, Yiquan

doi:10.3390/rs12193118

Cited by 28 publications

(10 citation statements)

References 52 publications

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“…It has become the benchmark to evaluate the performance of image classification algorithms [43]. Other datasets, such as UCAS-AOD [44], NWPU VHR-10 [45], and RSOD-Dataset [46], are also widely used in the field of deep learning.…”

Section: Introductionmentioning

confidence: 99%

Oil Well Detection via Large-Scale and High-Resolution Remote Sensing Images Based on Improved YOLO v4

et al. 2021

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Oil is an important resource for the development of modern society. Accurate detection of oil wells is of great significance to the investigation of oil exploitation status and the formulation of an exploitation plan. However, detecting small objects in large-scale and high-resolution remote sensing images, such as oil wells, is a challenging task due to the problems of large number, limited pixels, and complex background. In order to overcome this problem, first, we create our own oil well dataset to conduct experiments given the lack of a public dataset. Second, we provide a comparative assessment of two state-of-the-art object detection algorithms, SSD and YOLO v4, for oil well detection in our image dataset. The results show that both of them have good performance, but YOLO v4 has better accuracy in oil well detection because of its better feature extraction capability for small objects. In view of the fact that small objects are currently difficult to be detected in large-scale and high-resolution remote sensing images, this article proposes an improved algorithm based on YOLO v4 with sliding slices and discarding edges. The algorithm effectively solves the problems of repeated detection and inaccurate positioning of oil well detection in large-scale and high-resolution remote sensing images, and the accuracy of detection result increases considerably. In summary, this study investigates an appropriate algorithm for oil well detection, improves the algorithm, and achieves an excellent effect on a large-scale and high-resolution satellite image. It provides a new idea for small objects detection in large-scale and high-resolution remote sensing images.

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Section: Introductionmentioning

confidence: 99%

Oil Well Detection via Large-Scale and High-Resolution Remote Sensing Images Based on Improved YOLO v4

et al. 2021

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“…e calculation of F1-Score is shown in Journal of Electrical and Computer Engineering [35] Figure 5 in [35] 73. 16 33.5 UAV-YOLO [36] Figure 1 in [36] 74.68 30.12 RFN [37] ResNet-101 79.1 6.5 SigNMS [38] VGG-16 80.6 6.7 Improved-YOLOv3 [39] Figure 4 in [39] 86.42 25.8 MRFF-YOLO [40] Figure 5 in [40] 87. 16…”

Section: Resultsmentioning

confidence: 99%

Aircraft Detection for Remote Sensing Images Based on Deep Convolutional Neural Networks

Zhou

Yan

Shan

et al. 2021

Journal of Electrical and Computer Engineering

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Aircraft detection for remote sensing images, as one of the fields of computer vision, is one of the significant tasks of image processing based on deep learning. Recently, many high-performance algorithms for aircraft detection have been developed and applied in different scenarios. However, the proposed algorithms still have a series of problems; for instance, the algorithms will miss some small-scale aircrafts when applied to the remote sensing image. There are two main reasons for the problem; one reason is that the aircrafts in the remote sensing image are usually small in size, leading to detecting difficulty. The other reason is that the background of the remote sensing image is usually complex, so the algorithms applied to the scenario are easy to be affected by the background. To address the problem of small size, this paper proposes the Multiscale Detection Network (MSDN) which introduces a multiscale detection architecture to detect small-scale aircrafts. With the intention to resist the background noise, this paper proposes the Deeper and Wider Module (DAWM) which increases the perceptual field of the network to alleviate the affection. Besides, to address the two problems simultaneously, this paper introduces the DAWM into the MSDN and names the novel network structure as Multiscale Refined Detection Network (MSRDN). The experimental results show that the MSRDN method has detected the small-scale aircrafts that other algorithms missed and the performance indicators have higher performance than other algorithms.

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“…In the field of ORSIs, one-stage detectors are becoming increasingly popular. For example, MRFF-YOLO [22] introduced a multireceptive field model to enhance the performance of small-scale target extraction. Based on the SSD paradigm, AF-SSD [23] improves the performance of ORSI object detection by designing exquisite enhancement modules such as the encoding-decoding module and spatial and channel attention modules.…”

Section: One-stage Object Detection Methodsmentioning

confidence: 99%

Multi-Sector Oriented Object Detector for Accurate Localization in Optical Remote Sensing Images

et al. 2021

Remote Sensing

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Oriented object detection in optical remote sensing images (ORSIs) is a challenging task since the targets in ORSIs are displayed in an arbitrarily oriented manner and on small scales, and are densely packed. Current state-of-the-art oriented object detection models used in ORSIs primarily evolved from anchor-based and direct regression-based detection paradigms. Nevertheless, they still encounter a design difficulty from handcrafted anchor definitions and learning complexities in direct localization regression. To tackle these issues, in this paper, we proposed a novel multi-sector oriented object detection framework called MSO2-Det, which quantizes the scales and orientation prediction of targets in ORSIs via an anchor-free classification-to-regression approach. Specifically, we first represented the arbitrarily oriented bounding box as four scale offsets and angles in four quadrant sectors of the corresponding Cartesian coordinate system. Then, we divided the scales and angle space into multiple discrete sectors and obtained more accurate localization information by a coarse-granularity classification to fine-grained regression strategy. In addition, to decrease the angular-sector classification loss and accelerate the network’s convergence, we designed a smooth angular-sector label (SASL) that smoothly distributes label values with a definite tolerance radius. Finally, we proposed a localization-aided detection score (LADS) to better represent the confidence of a detected box by combining the category-classification score and the sector-selection score. The proposed MSO2-Det achieves state-of-the-art results on three widely used benchmarks, including the DOTA, HRSC2016, and UCAS-AOD data sets.

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

Cited by 28 publications

References 52 publications

Oil Well Detection via Large-Scale and High-Resolution Remote Sensing Images Based on Improved YOLO v4

Oil Well Detection via Large-Scale and High-Resolution Remote Sensing Images Based on Improved YOLO v4

Aircraft Detection for Remote Sensing Images Based on Deep Convolutional Neural Networks

Multi-Sector Oriented Object Detector for Accurate Localization in Optical Remote Sensing Images

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