面向空中目标拦截的轻量化弱小目标检测方法与应用

Aerial target interception technology relies on target detection and tracking techniques, with the detection of dim and small objects being a challenging aspect that directly affects the overall system performance. To address this issue, a lightweight dim and small object detection method is proposed for aerial target interception. Firstly, given the problem of limited global information for dim and small objects, the method is based on the YOLOv5 network, and the Swin Transformer is introduced to replace the C3 module in its architecture, thereby enhancing the network's ability to capture local information. Then, to compensate for diluted semantic information, a feature fusion network with cross-connection strategies is introduced to facilitate the fusion of feature maps at different scales to mitigate this problem. Finally, an additional upsampling is applied to the feature fusion network and high-resolution feature maps are fused to further improve the network's ability to detect dim and small objects. Furthermore, the DaSiamRPN neural network is incorporated for long-term tracking of dynamically dim and small objects. To ensure that the edge computing devices on unmanned aerial vehicles can perform model inference in real time, the model has been lightweighted on the basis of the aforementioned, and the large-scale object detection head of the model is removed to reduce the number of model parameters. Calculations show that the improved algorithm reduces the number of parameters by 21. 5% compared to the original YOLOv5 model. The experimental results on VisDrone2019 show that the proposed lightweight object detection algorithm performs better in detecting dim and small objects, achieving precision, recall, and mean average precision (mAP) of 96. 3%, 59%, and 40. 2%, respectively. These metrics are significantly higher than those of the original YOLOvSs algorithm and surpass those of current mainstream object detection algorithms. Meanwhile, generalization experiments are carried out on the TinyPerson datasets, and experimental results indicate a remarkable improvement in the dim and small object detection performance of the improved algorithm. To further validate the effectiveness of the proposed method, flight tests for aerial targets interception are conducted using unmanned aerial vehicle platforms. The results show that the method can effectively perform object detection and tracking tasks and successfully intercept targets, providing strong support for aerial target interception.