SAR Image Aircraft Target Recognition Based on Improved YOLOv5
Source abstract
Abstract
Synthetic aperture radar (SAR) is an active ground-surveillance radar system, which can observe targets regardless of time and weather. Passenger aircrafts are important targets for SAR, as it is of great importance for accurately recognizing the type of aircraft. SAR can provide dynamic monitoring of aircraft flights in civil aviation, which is helpful for the efficient management of airports. Due to the unique imaging characteristics of SAR, traditional target-detection algorithms have poor generalization ability, low detection accuracy, and a cumbersome recognition process. Target detection in high-resolution SAR images based on deep-learning methods is currently a major research hotspot. You Only Look Once v5 (YOLOv5) has the problems of missed detection and false alarms. In this study, we propose an improved version of YOLOv5. A multiscale feature adaptive fusion module is proposed to adaptively assign different weights to each scale of the feature layers, which can extract richer semantic and textural information. The SIOU loss function is proposed to replace the original CIOU loss function to speed up the convergence of the algorithm. The improved Ghost structure is proposed to optimize the YOLOv5 network to decrease the parameters of the model and the amount of computation. A coordinate attention (CA) module is incorporated into the backbone section to help extract useful information. The experimental results demonstrate that the improved YOLOv5 performs better in terms of detection without affecting calculation speed. The mean average precision (mAP) value of the improved YOLOv5 increased by 5.8% compared with the original YOLOv5.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Neural Network Applications · Advanced SAR Imaging Techniques · Infrared Target Detection Methodologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Electrical and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China
College of Electrical and Information Engineering, Beihua University, Jilin 132013, China
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100045, China
Funding
Ministry of Science and Technology of China · Science and Technology Development Plan Project of Jilin Province, China · Department of Education Science and Technology Research Project of Jilin Province, China
References (36)
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