Transactions of Nanjing University of Aeronautics & Astronautics
YOLO-v8 with Multidimensional Attention and Upsampling Fusion for Small Air Target Detection in Radar Images
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Abstract:
This study presents an innovative approach to improving the performance of YOLO-v8 model for small object detection in radar images. Initially， a local histogram equalization technique was applied to the original images， resulting in a notable enhancement in both contrast and detail representation. Subsequently， 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 fusion. At 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 accuracy. Additionally， 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 targets. Experimental 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 imagery. The results indicate a substantial improvement in accuracy compared to conventional methods such as image segmentation and standard convolutional neural networks.
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Project Supported:
This work was supported by the National Natural Science Foundation of China Joint Fund （No.U21B2028）， the National Key R&D Program of China （No.2021YFC 2100100）， and the Shanghai Science and Technology Project （Nos.21JC1403400， 23JC1402300）.
Clc Number:
TP751
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