Light-YOLOv4: An Edge-Device Oriented Target Detection Method for Remote Sensing Images

Ma, Xiaojie; Ji, Kefeng; Xiong, Boli; Zhang, Linbin; Feng, Sijia; Kuang, Gangyao

doi:10.1109/jstars.2021.3120009

Cited by 70 publications

(22 citation statements)

References 48 publications

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“…First, it is verified whether the proposed text center edge probability and text center direction can well separate pixels belonging to different text instances and combine pixels belonging to the same text instance. In the test, the model uses a single size test, and the long edge of the image is reduced to 1800. is study then compares the proposed method with other methods and confirms the proposed method in a broad context [23]. For multiscale experiments, the model downscales the long side of the image to {1000, 1800, 2600} pixels and mixes the multiscale results without maximum compression.…”

Section: Experiments and Analysismentioning

confidence: 54%

High-Precision Target Detection of Remote Sensing Image Based on Feature Enhancement with 6G Technology

Chen

2022

Advances in Multimedia

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We provide remote sensing image enhancement technology based on 6G nursing urine technology to solve the main problems and current challenges of target detection in remote sensing imaging. High-resolution images: to get a good image, first look at the remote control image, and objective findings on the remote control image play an important role in this process. Military and civilian use: at present, many advanced object detection algorithms have achieved success in natural imaging, but their development is limited by two factors: large variation in object size and low detection accuracy, the scale, rotation direction, and distribution density of remote sensing images. On the other hand, the first remote sensing image is usually a high-resolution, large-scale image, which requires more time than blank image detection. The model is fine-tuned on the ICDAR2015 training package for 60,000 repetitions. The same model experimentally reduced the image to 1280 × 768 and displayed the results in one dimension. Therefore, in order to solve the problem of low detection due to changes in azimuth, rotation direction, and velocity distribution, it is necessary to further study methods for detecting large-capacity remote sensing objects using abundant high-precision remote sensing technologies, sensing imaging service on remote sensing maps.

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Section: Experiments and Analysismentioning

confidence: 54%

High-Precision Target Detection of Remote Sensing Image Based on Feature Enhancement with 6G Technology

Chen

2022

Advances in Multimedia

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“…For example, Cui et al improved CenterNet with a spatial shuffling attention module to achieve a large-scale ship detection in the synthetic aperture radar (SAR) images [44]. To address the issue of difficult deployment of the existing models on the edge devices with limited memory resources, Ma et al proposed a lightweight object detector via compressing YOLOv4 [45]. To effectively identify ships with various scales in high-resolution optical remote sensing images, Li et al generated candidate ships from the feature maps using a region-proposal network [46].…”

Section: Ship Detection In Maritime Surveillance Systemmentioning

confidence: 99%

Light-SDNet: A Lightweight CNN Architecture for Ship Detection

Zhang

Rong

2022

IEEE Access

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Ship detection plays a vital role in monitoring and managing maritime safety. Most recently proposed learning-based object detection methods have achieved marked progress in detection accuracy, but the size of these models is too large to be applied to mobile devices with limited resources. Although some compact models have been presented in the previous study, they achieve unsatisfactory results in ship detection, especially under extreme weather conditions. To address these challenges, this article presents a lightweight convolutional neural network (CNN) called Light-SDNet to perform an end-to-end ship detection under different weather conditions. In the proposed model, we introduce the improved CA-Ghost, C3Ghost, and DepthWise Convolution (DWConv) into the You Only Look Once version 5 (YOLOv5) to reduce the number of model parameters, while remaining its powerful feature expression ability. We use parallel attention to highlight the features that contribute to the ship detection in the marine surveillance. To enhance the adaptability of the proposed model, a hybrid training strategy with generating synthetically-degraded images is proposed to augment the volume and diversity of the original datasets. The proposed strategy enables Light-SDNet to improve the ship detection results under severe weather conditions such as haze, rain, and low illumination. We compare Light-SDNet with other competitive approaches on a large-scaled ship dataset called SeaShips. We show that Light-SDNet achieves a better balance between the detection accuracy and the model complexity. The ship detection results on degraded marine images have proven the superior performance of the proposed model in terms of detection accuracy, robustness and efficiency.

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“…To further illustrate the performance improvement of the proposed method in object detection, we conduct target object detection experiments on fused images and singleangle images. Although deep learning-based object detection methods [40]- [43] have achieved remarkable achievements in SAR images, they are all data-driven. In our experiments, the two-parameter CFAR (2p-CFAR) detector [44] Table 3 lists the targets detection performance of singleangle (SA) images and fused images.…”

Section: ) Target Detection Performance Of Single-angle Image and Fus...mentioning

confidence: 99%

A Novel Shadow and Layover Segmentation Network for Multi-Angle SAR Images Fusion

Zhang

Yin

et al. 2022

IEEE Access

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Shadow and layover are geometric distortion phenomenons in side-view imaging synthetic aperture radar (SAR) systems, especially in mountainous areas and densely populated urban areas. The shadow can block the target of the observation area, making it impossible to obtain the scattering characteristics of the target. The layover causes phase distortion and alters target characteristics. Shadow and layover severely hinder the interpretation of SAR images. To confront the above problems, a multi-angle fusion algorithm based on unsupervised progressive segmentation network is proposed. Firstly, inspired by mega-constellations of low earth orbit, a spaceborne SAR collaborative observation model is proposed to generate multi-angle images of fluctuant terrain. Secondly, according to the difference of echos in the shadow and layover regions, an unsupervised progressive segmentation network is designed to sequentially segment the shadow and layover regions. Finally, to improve the contrast and brightness of the fused SAR image, a single-scale weighted fusion algorithm is designed. Experiments were conducted using the simulated multi-angle SAR images. Compared with single-angle images, the accuracy of target detection and figure-of-merit of the fused SAR image are significantly higher than those of other methods.INDEX TERMS Shadow and layover, synthetic aperture radar, unsupervised progressive segmentation network, multi-angle fusion.

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Light-YOLOv4: An Edge-Device Oriented Target Detection Method for Remote Sensing Images

Cited by 70 publications

References 48 publications

High-Precision Target Detection of Remote Sensing Image Based on Feature Enhancement with 6G Technology

High-Precision Target Detection of Remote Sensing Image Based on Feature Enhancement with 6G Technology

Light-SDNet: A Lightweight CNN Architecture for Ship Detection

A Novel Shadow and Layover Segmentation Network for Multi-Angle SAR Images Fusion

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