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Lira-YOLO: a lightweight model for ship detection in radar images

Journal of Systems Engineering and Electronics 2020年第5期31卷 950-956页

作者： ZHOU Long WEI Suyuan CUI Zhongma FANG Jiaqi YANG Xiaoting DING Wei Beijing Institute of Remote Sensing Equipment Beijing 100854China Xi’an High-tech Research Institute Xi’an 710025China School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China

For the detection of marine ship objects in radar images, large-scale networks based on deep learning are difficult to be deployed on existing radar-equipped devices. This paper proposes a lightweight convolutional neural network, LiraNet, which combines the idea of dense connections, residual connections and group convolution, including stem blocks and extractor *** designed stem block uses a series of small convolutions to extract the input image features, and the extractor network adopts the designed two-way dense connection module, which further reduces the network operation complexity. Mounting LiraNet on the object detection framework Darknet, this paper proposes Lira-you only look once(Lira-YOLO), a lightweight model for ship detection in radar images, which can easily be deployed on the mobile devices. Lira-YOLO's prediction module uses a two-layer YOLO prediction layer and adds a residual module for better feature delivery. At the same time, in order to fully verify the performance of the model, mini-RD, a lightweight distance Doppler domain radar images dataset, is constructed. Experiments show that the network complexity of Lira-YOLO is low, being only 2.980 Bflops, and the parameter quantity is smaller, which is only 4.3 MB. The mean average precision(mAP) indicators on the mini-RD and SAR ship detection dataset(SSDD) reach 83.21% and 85.46%, respectively,which is comparable to the tiny-YOLOv3. Lira-YOLO has achieved a good detection accuracy with less memory and computational cost.

关键词： lightweight radar images ship detection you only look once(YOLO)

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YOLO‑v8 with Multidimensional Attention and Upsampling Fusion for Small Air Target Detection in radar images

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Transactions of Nanjing University of Aeronautics and Astronautics 2024年第6期41卷 710-724页

作者： JIANG Zhenyu LI Xiaodong DU Chen CHEN An HAN Yanqiang LI Jinjin College of Aerospace Science National University of Defense TechnologyChangsha 410073P.R.China National Key Laboratory of Advanced Micro and Nano Manufacture Technology Shanghai Jiao Tong UniversityShanghai 200240P.R.China Department of Micro/Nano Electronics School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghai 200240P.R.China

This study presents an innovative approach to improving the performance of YOLO-v8 model for small object detection in radar ***,a local histogram equalization technique was applied to the original images,resulting in a notable enhancement in both contrast and detail ***,the YOLO-v8 backbone network was augmented by incorporating convolutional kernels based on a multidimensional attention mechanism and a parallel processing strategy,which facilitated more effective feature information *** the model’s head,an upsampling layer was added,along with the fusion of outputs from the shallow network,and a detection head specifically tailored for small object detection,thereby further improving ***,the loss function was modified to incorporate focal-intersection over union(IoU)in conjunction with scaled-IoU,which enhanced the model’s performance.A weighting strategy was also introduced,effectively improving detection accuracy for small *** results demonstrate that the customized model outperforms traditional approaches across various evaluation metrics,including recall,precision,F1-score,and the receiver operating characteristic(ROC)curve,validating its efficacy and innovation in small object detection within radar *** results indicate a substantial improvement in accuracy compared to conventional methods such as image segmentation and standard convolutional neural networks.

关键词： YOLO radar images object detection machine learning

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