Improved YOLOv5 Based on Hybrid Domain Attention for Small Object Detection in Optical Remote Sensing Images

Deng, Tianmin; Liu, Xuhui; Mao, Guangjun

doi:10.3390/electronics11172657

Cited by 8 publications

(4 citation statements)

References 34 publications

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“…By integrating Channel Attention and Spatial Attention, the Channel and Spatial Mixed Attention can better focus on the target with relatively less pixels and promote the accuracy of small target detection. Deng et al 25 introduced a method to detect small targets in the Optical Remote Sensing, which combines the hybrid domain attention units (HDAUs) of channel attention and spatial attention, thereby improving feature extraction ability and suppress background noise. Self-Attention principally focuses on the dependence relations in the input sequence to pick up the modelling capacity sequence data.…”

Section: Related Workmentioning

confidence: 99%

Metal surface defect detection based on improved YOLOv5

Zhou,

Lu,

et al. 2023

Sci Rep

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During the production of metal material, various complex defects may come into being on the surface, together with large amount of background texture information, causing false or missing detection in the process of small defect detection. To resolve those problems, this paper introduces a new model which combines the advantages of CSPlayer module and Global Attention Enhancement Mechanism based on the YOLOv5s model. First of all, we replace C3 module with CSPlayer module to augment the neural network model, so as to improve its flexibility and adaptability. Then, we introduce the Global Attention Mechanism (GAM) and build the generalized additive model. In the meanwhile, the attention weights of all dimensions are weighted and averaged as output to promote the detection speed and accuracy. The results of the experiment in which the GC10-DET augmented dataset is involved, show that the improved algorithm model performs better than YOLOv5s in precision, mAP@0.5 and mAP@0.5: 0.95 by 5.3%, 1.4% and 1.7% respectively, and it also has a higher reasoning speed.

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Section: Related Workmentioning

confidence: 99%

Metal surface defect detection based on improved YOLOv5

Zhou,

Lu,

et al. 2023

Sci Rep

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“…Deng et al [28] designed an Extended Feature Pyramid Network (EFPN) specifically for small-object detection, which contained a Feature Texture Transfer (FTT) module that acted on the super-resolution feature map by extracting semantic information and texture features from the feature map of the FPN network, thus effectively improving the representation of small-object feature information and being efficient in both computation and storage. Deng et al [29] proposed a multi-scale dynamic weighted feature fusion network, which adaptively assigns different weights to feature layers at different scales through network training to increase the contribution of shallow feature information in the whole network, which directs the model for small-object detection tasks.…”

Section: Related Workmentioning

confidence: 99%

Remote Sensing Small Object Detection Network Based on Attention Mechanism and Multi-Scale Feature Fusion

Tang

Zhang

et al. 2023

Remote Sensing

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In remote sensing images, small objects have too few discriminative features, are easily confused with background information, and are difficult to locate, leading to a degradation in detection accuracy when using general object detection networks for aerial images. To solve the above problems, we propose a remote sensing small object detection network based on the attention mechanism and multi-scale feature fusion, and name it AMMFN. Firstly, a detection head enhancement module (DHEM) was designed to strengthen the characterization of small object features through a combination of multi-scale feature fusion and attention mechanisms. Secondly, an attention mechanism based channel cascade (AMCC) module was designed to reduce the redundant information in the feature layer and protect small objects from information loss during feature fusion. Then, the Normalized Wasserstein Distance (NWD) was introduced and combined with Generalized Intersection over Union (GIoU) as the location regression loss function to improve the optimization weight of the model for small objects and the accuracy of the regression boxes. Finally, an object detection layer was added to improve the object feature extraction ability at different scales. Experimental results from the Unmanned Aerial Vehicles (UAV) dataset VisDrone2021 and the homemade dataset show that the AMMFN improves the APs values by 2.4% and 3.2%, respectively, compared with YOLOv5s, which represents an effective improvement in the detection accuracy of small objects.

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“…The convolutional layers are stacked with multiple 3 × 3 and 2 × 2 convolutional kernels instead of the traditional large convolutional kernels [14]. The parameters and computation are successfully reduced by the small convolutional kernels instead of the large convolutional kernels, and RMSProp is used as an optimizer so that the model can be better adapted to complex activation function variables with high complexity computations [15].…”

Section: Model Introductionmentioning

confidence: 99%

Surface Crack Detection of Steel Structures in Railroad Industry Based on Multi-Model Training Comparison Technique

et al. 2023

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A method of steel structure surface crack identification based on artificial intelligence technology is proposed to solve the problem that steel cracks can not be detected and forewarned in time when they appear in the railway industrial environment. The appearance of steel cracks greatly weakens the stability of steel structures, and will seriously endanger the safety of the railway industry if it is not detected and repaired in time. However, the common steel crack detection methods cannot achieve real-time monitoring of steel structures. In order to monitor the surface of steel structure in real-time and explore the recognition effect and model the advantages of common classification neural network models for surface cracks of railway industrial steel, this study evaluates the network model with multiple indicators and parameters under two experimental conditions. In this study, the steel surface cracks in the railway industrial environment are taken as samples, and the steel cracks are identified through the neural network model. For large-volume datasets, the recognition accuracy of the three network models has reached 97%, of which the YOLOv5 model has the best comprehensive recognition ability, and the C-Alex model has the best performance and convergence speed in small-volume datasets. This study explores the application prospects of models under different scenarios, proving that the three models can effectively detect steel surface cracks in real-time, and at the same time, it will pave the way for the development and application of artificial intelligence multi-model fusion technology in the field of the railway industry.

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Improved YOLOv5 Based on Hybrid Domain Attention for Small Object Detection in Optical Remote Sensing Images

Cited by 8 publications

References 34 publications

Metal surface defect detection based on improved YOLOv5

Metal surface defect detection based on improved YOLOv5

Remote Sensing Small Object Detection Network Based on Attention Mechanism and Multi-Scale Feature Fusion

Surface Crack Detection of Steel Structures in Railroad Industry Based on Multi-Model Training Comparison Technique

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