Ship Target Detection in Optical Remote Sensing Images Based on Multiscale Feature Enhancement

Zhou, Liming; Li, Yahui; Rao, Xiaohan; Liu, Cheng; Zuo, Xianyu; Liu, Yang

doi:10.1155/2022/2605140

Cited by 6 publications

(5 citation statements)

References 34 publications

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“…Therefore, the dilated convolution is developed as the context information mining method, as shown in Figure 11b. Xu et al [77], Chen et al [78], and Zhou et al [79] used dilated convolution instead of regular convolution to extract ship features. Dilated convolution can capture more context information without bringing too many parameters, introducing more references in SDORSIs.…”

Section: Dilated-convolution-based Methodmentioning

confidence: 99%

See 1 more Smart Citation

Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances

Zhao,

Wang,

et al. 2024

Remote Sensing

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Ship detection aims to automatically identify whether there are ships in the images, precisely classifies and localizes them. Regardless of whether utilizing early manually designed methods or deep learning technology, ship detection is dedicated to exploring the inherent characteristics of ships to enhance recall. Nowadays, high-precision ship detection plays a crucial role in civilian and military applications. In order to provide a comprehensive review of ship detection in optical remote-sensing images (SDORSIs), this paper summarizes the challenges as a guide. These challenges include complex marine environments, insufficient discriminative features, large scale variations, dense and rotated distributions, large aspect ratios, and imbalances between positive and negative samples. We meticulously review the improvement methods and conduct a detailed analysis of the strengths and weaknesses of these methods. We compile ship information from common optical remote sensing image datasets and compare algorithm performance. Simultaneously, we compare and analyze the feature extraction capabilities of backbones based on CNNs and Transformer, seeking new directions for the development in SDORSIs. Promising prospects are provided to facilitate further research in the future.

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Section: Dilated-convolution-based Methodmentioning

confidence: 99%

“…There are gaps in the dilated convolution kernel, which leads to information discontinuity. [77][78][79] Feature Fusion…”

Section: Context Information Miningmentioning

confidence: 99%

Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances

Zhao,

Wang,

et al. 2024

Remote Sensing

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“…The background of the smoke data is complex, and interference from clouds and backgrounds can cause the model to produce false alarms [48]. In this study, the other bands of Sentinel-2 will be added to the red, green, and blue bands as training sample data.…”

Section: Separation Methodsmentioning

confidence: 99%

Detection of Smoke from Straw Burning Using Sentinel-2 Satellite Data and an Improved YOLOv5s Algorithm

Liu

et al. 2023

Remote Sensing

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The burning of straw is a very destructive process that threatens people’s livelihoods and property and causes irreparable environmental damage. It is therefore essential to detect and control the burning of straw. In this study, we analyzed Sentinel-2 data to select the best separation bands based on the response characteristics of clouds, smoke, water bodies, and background (vegetation and bare soil) to the different bands. The selected bands were added to the red, green, and blue bands (RGB) as training sample data. The band that featured the highest detection accuracy, RGB_Band6, was finally selected, having an accuracy of 82.90%. The existing object detection model cannot directly handle multi-band images. This study modified the input layer structure based on the YOLOv5s model to build an object detection network suitable for multi-band remote sensing images. The Squeeze-and-Excitation (SE) network attention mechanism was introduced based on the YOLOv5s model so that the delicate features of smoke were enhanced, and the Convolution + Batch normalization + Leaky ReLU (CBL) module was replaced with the Convolution + Batch normalization + Mish (CBM) module. The accuracy of the model was improved to 75.63%, which was 1.81% better than before. We also discussed the effect of spatial resolution on model detection and where accuracies of 84.18%, 73.13%, and 45.05% for images of 60-, 20-, and 10-m resolution, respectively, were realized. The experimental results demonstrated that the accuracy of the model only sometimes improved with increasing spatial resolution. This study provides a technical reference for the monitoring of straw burning, which is vital for both the control of straw burning and ways to improve ambient air quality.

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“…The multi-branch convolution layer provided different-sized receptive fields for the input characteristic map through cavity convolution. Then each layer passed through the BN layer and ReLU activation layer, respectively, and finally, the average pooling layer was used to fuse the information from the three branch receptive fields to improve the accuracy of multi-scale prediction [ 35 ].…”

Section: Methodsmentioning

confidence: 99%

Apple-Net: A Model Based on Improved YOLOv5 to Detect the Apple Leaf Diseases

Zhu

et al. 2022

Plants

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Effective identification of apple leaf diseases can reduce pesticide spraying and improve apple fruit yield, which is significant to agriculture. However, the existing apple leaf disease detection models lack consideration of disease diversity and accuracy, which hinders the application of intelligent agriculture in the apple industry. In this paper, we explore an accurate and robust detection model for apple leaf disease called Apple-Net, improving the conventional YOLOv5 network by adding the Feature Enhancement Module (FEM) and Coordinate Attention (CA) methods. The combination of the feature pyramid and pan in YOLOv5 can obtain richer semantic information and enhance the semantic information of low-level feature maps but lacks the output of multi-scale information. Thus, the FEM was adopted to improve the output of multi-scale information, and the CA was used to improve the detection efficiency. The experimental results show that Apple-Net achieves a higher mAP@0.5 (95.9%) and precision (93.1%) than four classic target detection models, thus proving that Apple-Net achieves more competitive results on apple leaf disease identification.

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Ship Target Detection in Optical Remote Sensing Images Based on Multiscale Feature Enhancement

Cited by 6 publications

References 34 publications

Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances

Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances

Detection of Smoke from Straw Burning Using Sentinel-2 Satellite Data and an Improved YOLOv5s Algorithm

Apple-Net: A Model Based on Improved YOLOv5 to Detect the Apple Leaf Diseases

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