YOLOv3_ReSAM: A Small-Target Detection Method

Small targets in long-distance aerial photography have the problems of small size and blurry appearance, and traditional object detection algorithms face great challenges in the field of small-object detection. With the collection of massive data in the information age, traditional object detection...

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Bibliographic Details
Published inElectronics (Basel) Vol. 11; no. 10; p. 1635
Main Authors Liu, Bailin, Luo, Huan, Wang, Haotong, Wang, Shaoxu
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.05.2022
Subjects
Accuracy
Aerial photography
Algorithms
Altitude
Border patrol
Computer networks
Deep learning
Efficiency
Machine learning
Multilayers
Neural networks
Object recognition
Regression models
Semantics
Target detection
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Summary:Small targets in long-distance aerial photography have the problems of small size and blurry appearance, and traditional object detection algorithms face great challenges in the field of small-object detection. With the collection of massive data in the information age, traditional object detection algorithms have been gradually replaced by deep learning algorithms and have an advantage. In this paper, the YOLOV3-Tiny backbone network is augmented by using the pyramid structure of image features to achieve multi-level feature fusion prediction. In order to eliminate the loss of spatial feature information and hierarchical information caused by pooling operations in convolution processes and multi-scale operations in multi-layer structures, a spatial attention mechanism based on residual structure is proposed. At the same time, the idea of reinforcement learning is introduced to guide bounding box regression on the basis of the rough positioning of the native boundary regression strategy, and the variable IoU calculation method is used as the evaluation index of the reward function, and the boundary regression model based on the reward mechanism is proposed for fine adjustment. The VisDrone2019 data set was selected as the experimental data support. Experimental results show that the mAP value of the improved small-object detection model is 33.15%, which is 11.07% higher than that of the native network model, and the boundary regression accuracy is improved by 23.74%.
ISSN:2079-9292
2079-9292
DOI:10.3390/electronics11101635