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| A Lightweight Aerial Rotated Object Detection Algorithm of Air-to-Surface Missile |
| LIU Jing1， GUO Xiaolei2， ZHANG Xinhai2， MAO Jingjun1， LYU Ruiheng3 |
| 1. School of Electrical and Information Engineering， Tianjin University， Tianjin 300072， China；
2. China Academy of Electronics and Information Technology， Beijing 100041， China；
3. Shanghai Electro-Mechanical Engineering Institute， Shanghai 201109， China |
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Abstract With the evolution of modern military and security challenges, the variety and direction of ground targets increase. Traditional target detection algorithms are facing numerous limitations in complex and dynamic aerial environments, and the complexity of neural networks challenges their application on mobile devices. Therefore, to enhance the accuracy and efficiency of detecting ground targets from any direction during flight, a lightweight aerial rotated object detection algorithm (LRODA) was proposed in this study. Firstly, the Ghost module was employed to enhance the original convolutional neural network and generated more feature maps with fewer small filters, thus reducing computational costs. Secondly, the improved convolutional network was used to generate high-quality proposals from the feature pyramid network at five different levels. Finally, the detection head harvested the generated proposals as input and ultimately used for classification and regression. Experimental results prove that the proposed rotated object detection algorithm can locate ground targets accurately and reduce model complexity effectively.
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Received: 04 March 2024
Published: 10 September 2024
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2024, Vol. 7 
