Multi-scale ship target detection using SAR images based on improved Yolov5

Yasir, Muhammad; Liu, Shanwei; Xu, Mingming; Song, Hui; Hossain, Md Sakaouth; Colak, Arife Tugsan Isiacik; Wang, Dawei; Wan, Jianhua; Dang, Kinh Bac

doi:10.3389/fmars.2022.1086140

Cited by 37 publications

(13 citation statements)

References 65 publications

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“…The backbone employs operations such as convolution and pooling to reduce feature map dimensions, increase depth, and incorporate the CBAM C3 module for automatic attention to image features. Feature fusion (Neck) is achieved using a PAN + FPN 27 structure, merging feature maps with different resolutions and rich semantic information, creating a feature pyramid.…”

Section: Methodsmentioning

confidence: 99%

Machine vision-based autonomous road hazard avoidance system for self-driving vehicles

Qiu,

Tang,

Yang

et al. 2024

Sci Rep

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The resolution of traffic congestion and personal safety issues holds paramount importance for human’s life. The ability of an autonomous driving system to navigate complex road conditions is crucial. Deep learning has greatly facilitated machine vision perception in autonomous driving. Aiming at the problem of small target detection in traditional YOLOv5s, this paper proposes an optimized target detection algorithm. The C3 module on the algorithm’s backbone is upgraded to the CBAMC3 module, introducing a novel GELU activation function and EfficiCIoU loss function, which accelerate convergence on position loss lbox, confidence loss lobj, and classification loss lcls, enhance image learning capabilities and address the issue of inaccurate detection of small targets by improving the algorithm. Testing with a vehicle-mounted camera on a predefined route effectively identifies road vehicles and analyzes depth position information. The avoidance model, combined with Pure Pursuit and MPC control algorithms, exhibits more stable variations in vehicle speed, front-wheel steering angle, lateral acceleration, etc., compared to the non-optimized version. The robustness of the driving system's visual avoidance functionality is enhanced, further ameliorating congestion issues and ensuring personal safety.

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Section: Methodsmentioning

confidence: 99%

Machine vision-based autonomous road hazard avoidance system for self-driving vehicles

Qiu,

Tang,

Yang

et al. 2024

Sci Rep

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“…We used the deep learning object detection algorithm YOLOv5 without modifications from the GitHub repository of the same name using the PyTorch framework as of 8 December 2021 [29]. YOLOv5 represents a balanced trade-off between speed and accuracy compared to other commonly used algorithms [30,31]. This algorithm included data augmentation and hyperparameters suited for fine-tuning by default, as well as the capability to ensemble multiple models.…”

Section: Algorithm Implementationmentioning

confidence: 99%

Application of YOLOv5 for Point Label Based Object Detection of Black Pine Trees with Vitality Losses in UAV Data

et al. 2023

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Monitoring tree diseases in forests is crucial for managing pathogens, particularly as climate change and globalization lead to the emergence and spread of tree diseases. Object detection algorithms for monitoring tree diseases through remote sensing rely on bounding boxes to represent trees. However, this approach may not be the most efficient. Our study proposed a solution to this challenge by applying object detection to unmanned aerial vehicle (UAV)-based imagery, using point labels that were converted into equally sized square bounding boxes. This allowed for effective and extensive monitoring of black pine (Pinus nigra L.) trees with vitality-related damages. To achieve this, we used the “You Only Look Once’’ version 5 (YOLOv5) deep learning algorithm for object detection, alongside a 16 by 16 intersection over union (IOU) and confidence threshold grid search, and five-fold cross-validation. Our dataset used for training and evaluating the YOLOv5 models consisted of 179 images, containing a total of 2374 labeled trees. Our experiments revealed that, for achieving the best results, the constant bounding box size should cover at least the center half of the tree canopy. Moreover, we found that YOLOv5s was the optimal model architecture. Our final model achieved competitive results for detecting damaged black pines, with a 95% confidence interval of the F1 score of 67–77%. These results can possibly be improved by incorporating more data, which is less effort-intensive due to the use of point labels. Additionally, there is potential for advancements in the method of converting points to bounding boxes by utilizing more sophisticated algorithms, providing an opportunity for further research. Overall, this study presents an efficient method for monitoring forest health at the single tree level, using point labels on UAV-based imagery with a deep learning object detection algorithm.

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“…In contrast, the one-stage object detection algorithm boasts rapid detection speed and strong real-time performance, as it operates without the need for candidate regions. Tang et al (2023) improved the YOLOv7 model for detecting plums in natural environment by adding the attention mechanism and modifying the upsampling function, which improved mAP by 2.03%; Muhammad et al (2023) Using the C3 and FPN + PAN structures and attention mechanism, the original YOLOv5 model has been enhanced in the backbone and neck section to achieve high identification rates. Liu et al (2022) further expanded the model to 3 billion parameters by leveraging the Swin Transformer ( Liu et al, 2021 ) architecture, allowing it to accommodate training images with resolutions up to 1,536 × 1,536.…”

Section: Introductionmentioning

confidence: 99%

An improved lightweight object detection algorithm for YOLOv5

Luo,

Wei,

Wang

et al. 2024

PeerJ Computer Science

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Object detection based on deep learning has made great progress in the past decade and has been widely used in various fields of daily life. Model lightweighting is the core of deploying target detection models on mobile or edge devices. Lightweight models have fewer parameters and lower computational costs, but are often accompanied by lower detection accuracy. Based on YOLOv5s, this article proposes an improved lightweight target detection model, which can achieve higher detection accuracy with smaller parameters. Firstly, utilizing the lightweight feature of the Ghost module, we integrated it into the C3 structure and replaced some of the C3 modules after the upsample layer on the neck network, thereby reducing the number of model parameters and expediting the model’s inference process. Secondly, the coordinate attention (CA) mechanism was added to the neck to enhance the model’s ability to pay attention to relevant information and improved detection accuracy. Finally, a more efficient Simplified Spatial Pyramid Pooling—Fast (SimSPPF) module was designed to enhance the stability of the model and shorten the training time of the model. In order to verify the effectiveness of the improved model, experiments were conducted using three datasets with different features. Experimental results show that the number of parameters of our model is significantly reduced by 28% compared with the original model, and mean average precision (mAP) is increased by 3.1%, 1.1% and 1.8% respectively. The model also performs better in terms of accuracy compared to existing lightweight state-of-the-art models. On three datasets with different features, mAP of the proposed model achieved 87.2%, 77.8% and 92.3%, which is better than YOLOv7tiny (81.4%, 77.7%, 90.3%), YOLOv8n (84.7%, 77.7%, 90.6%) and other advanced models. When achieving the decreased number of parameters, the improved model can successfully increase mAP, providing great reference for deploying the model on mobile or edge devices.

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Multi-scale ship target detection using SAR images based on improved Yolov5

Cited by 37 publications

References 65 publications

Machine vision-based autonomous road hazard avoidance system for self-driving vehicles

Machine vision-based autonomous road hazard avoidance system for self-driving vehicles

Application of YOLOv5 for Point Label Based Object Detection of Black Pine Trees with Vitality Losses in UAV Data

An improved lightweight object detection algorithm for YOLOv5

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