YOLOv3 Remote Sensing SAR Ship Image Detection

Chaudhary, Yash; Mehta, Manan; Goel, Nikki; Bhardwaj, Parul; Gupta, Deepak; Khanna, Ashish

doi:10.1007/978-981-15-8335-3_40

Cited by 9 publications

(5 citation statements)

References 9 publications

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“…The limitation of this model is that its performance is limited when the input images are affected by various climatic conditions. Yash Chaudhary et al [19] proposed a model based on YOLOv2. This model proved that the average precision (AP) score of YOLOv3, which was around 90.25, was comparatively greater than that of YOLOv2, which produced around 90.05.…”

Section: Methodsmentioning

confidence: 99%

Hash-Based Deep Learning Approach for Remote Sensing Satellite Imagery Detection

Gadamsetty

Rupa

et al. 2022

Water

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Ship detection plays a crucial role in marine security in remote sensing imagery. This paper discusses about a deep learning approach to detect the ships from satellite imagery. The model developed in this work achieves integrity by the inclusion of hashing. This model employs a supervised image classification technique to classify images, followed by object detection using You Only Look Once version 3 (YOLOv3) to extract features from deep CNN. Semantic segmentation and image segmentation is done to identify object category of each pixel using class labels. Then, the concept of hashing using SHA-256 is applied in conjunction with the ship count and location of bounding box in satellite image. The proposed model is tested on a Kaggle Ships dataset, which consists of 231,722 images. A total of 70% of this data is used for training, and the 30% is used for testing. To add security to images with detected ships, the model is enhanced by hashing using SHA-256 algorithm. Using SHA-256, which is a one-way hash, the data are split up into blocks of 64 bytes. The input data to the hash function are both the ship count and bounding box location. The proposed model achieves integrity by using SHA-256. This model allows secure transmission of highly confidential images that are tamper-proof.

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Section: Methodsmentioning

confidence: 99%

Hash-Based Deep Learning Approach for Remote Sensing Satellite Imagery Detection

Gadamsetty

Rupa

et al. 2022

Water

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“…Then channel attention and space attention are combined to re-weight the extracted features, improving the significance of the target features and suppressing the interference of noise, and finally connect them to each layer of the pyramid laterally. Chaudhary et al ( 2021 ) tried to directly apply YOLOv3 (Redmon and Farhadi, 2018 ) to ship detection and achieved some good results. Inspired by YOLO, Zhang and Zhang ( 2019 ) divided the original image into grid regions, and each grid was independently responsible for detecting the target in the region.…”

Section: Related Workmentioning

confidence: 99%

Context-aware SAR image ship detection and recognition network

Li,

Yue,

et al. 2024

Front. Neurorobot.

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With the development of deep learning, synthetic aperture radar (SAR) ship detection and recognition based on deep learning have gained widespread application and advancement. However, there are still challenging issues, manifesting in two primary facets: firstly, the imaging mechanism of SAR results in significant noise interference, making it difficult to separate background noise from ship target features in complex backgrounds such as ports and urban areas; secondly, the heterogeneous scales of ship target features result in the susceptibility of smaller targets to information loss, rendering them elusive to detection. In this article, we propose a context-aware one-stage ship detection network that exhibits heightened sensitivity to scale variations and robust resistance to noise interference. Then we introduce a Local feature refinement module (LFRM), which utilizes multiple receptive fields of different sizes to extract local multi-scale information, followed by a two-branch channel-wise attention approach to obtain local cross-channel interactions. To minimize the effect of a complex background on the target, we design the global context aggregation module (GCAM) to enhance the feature representation of the target and suppress the interference of noise by acquiring long-range dependencies. Finally, we validate the effectiveness of our method on three publicly available SAR ship detection datasets, SAR-Ship-Dataset, high-resolution SAR images dataset (HRSID), and SAR ship detection dataset (SSDD). The experimental results show that our method is more competitive, with AP50s of 96.3, 93.3, and 96.2% on the three publicly available datasets, respectively.

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“…A primary limitation of this methodology is its propensity to induce delays in the process of decisionmaking, particularly in comparison to alternative models. Chaudhary et al [44] presented a YOLOv2-based model. This model demonstrated that YOLOv3's average accuracy (AP) score, which was about 90.25, was significantly higher than YOLOv2's, which generated an AP score of approximately 90.05.…”

Section: Related Workmentioning

confidence: 99%

A Novel Bottleneck Residual and Self-Attention Fusion-Assisted Architecture for Land Use Recognition in Remote Sensing Images

Hamza,

Khan,

ur Rehman

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The massive yearly population growth is causing hazards to spread swiftly around the world and have a detrimental impact on both human life and the world economy. By ensuring early prediction accuracy, remote sensing enters the scene to safeguard the globe against weather-related threats and natural disasters. Convolutional neural networks, which are a reflection of deep learning, have been used more recently to reliably identify land use in remote sensing images. This work proposes a novel bottleneck residual and self-attention fusion-assisted architecture for land use recognition from remote sensing images. First, we proposed using the fast neural approach to generate cloud-effect satellite images. In neural style, we proposed a 5-layered residual block CNN to estimate the loss of neural-style images. After that, we proposed two novel architectures, named 3-layered bottleneck CNN architecture and 3-layered bottleneck self-attention CNN architecture, for the classification of land use images. Training has been conducted on both proposed and original neural-style generated datasets for both architectures. Subsequently, features are extracted from the deep layers and merged employing an innovative serial approach based on weighted entropy. By removing redundant and superfluous data, a novel Chimp Optimization technique is applied to the fused features in order to further refine them. In conclusion, selected features are classified using the help of neural network classifiers. The experimental procedure yielded respective accuracy rates of 99.0% and 99.4% when applied to both datasets. When evaluated in comparison to state-of-the-art (SOTA) methods, the outcomes generated by the proposed framework demonstrated enhanced precision and accuracy.

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YOLOv3 Remote Sensing SAR Ship Image Detection

Cited by 9 publications

References 9 publications

Hash-Based Deep Learning Approach for Remote Sensing Satellite Imagery Detection

Hash-Based Deep Learning Approach for Remote Sensing Satellite Imagery Detection

Context-aware SAR image ship detection and recognition network

A Novel Bottleneck Residual and Self-Attention Fusion-Assisted Architecture for Land Use Recognition in Remote Sensing Images

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