UAV Target Segmentation Based on Depse Unet++ Modeling

To enhance the capabilities of UAV platforms in recognizing and tracking hostile targets on the battlefield, advanced feature extraction and image segmentation are required. In response, the Depse Unet++ model was developed. By introducing Squeeze-and-Excitation, the model’s ability to discriminate camouflaged targets in high-similarity backgrounds is improved; by incorporating a depth-separated convolutional design, the parameters and computational requirements for embedded device applications are significantly reduced; and employing Dropout technique to prevent overfitting with limited sample sizes, thus boosting the model’s adaptability and generalization across environments. Evaluations were conducted using a custom-built dataset to test the model’s segmentation accuracy and performance. The experimental results show that the Depse Unet++ model surpasses existing state-of-the-art models in critical metrics such as Mean Inter-section and Union Ratio, Dice coefficient and pixel accuracy. Additionally, the specific contributions of each technical component to the model’s performance were confirmed through ablation studies, ensuring the method’s validity and reliability. Overall, the Depse Unet++ model offers a more effective tool for target recognition and semantic segmentation for UAVs operating in complex battlefield scenarios.