Qiu Yijuan, Zheng Xiangyue, Hao Xuying, Zhang Gang, Lei Tao, Jiang Ping
National Laboratory on Adaptive Optics, Chengdu 610209, China.
University of Chinese Academy of Sciences, Beijing 101408, China.
Sensors (Basel). 2024 Nov 23;24(23):7472. doi: 10.3390/s24237472.
Remote sensing images play a vital role in domains including environmental monitoring, agriculture, and autonomous driving. However, the detection of targets in remote sensing images remains a challenging task. This study introduces innovative methods to enhance feature extraction, feature fusion, and model optimization. The Adaptive Selective Feature Enhancement Module (AFEM) dynamically adjusts feature weights using GhostModule and sigmoid functions, thereby enhancing the accuracy of small target detection. Moreover, the Adaptive Multi-scale Convolution Kernel Feature Fusion Module (AKSFFM) enhances feature fusion through multi-scale convolution operations and attention weight learning mechanisms. Moreover, our proposed ARSOD-YOLO optimized the network architecture, component modules, and loss functions based on YOLOv8, enhancing outstanding small target detection capabilities while preserving model efficiency. We conducted experiments on the VEDAI and AI-TOD datasets, showcasing the excellent performance of ARSOD-YOLO. Our algorithm achieved an mAP50 of 74.3% on the VEDAI dataset, surpassing the YOLOv8 baseline by 3.1%. Similarly, on the AI-TOD dataset, the mAP50 reached 47.8%, exceeding the baseline network by 6.1%.
遥感图像在环境监测、农业和自动驾驶等领域发挥着至关重要的作用。然而，遥感图像中的目标检测仍然是一项具有挑战性的任务。本研究引入了创新方法来增强特征提取、特征融合和模型优化。自适应选择性特征增强模块（AFEM）使用GhostModule和sigmoid函数动态调整特征权重，从而提高小目标检测的准确性。此外，自适应多尺度卷积核特征融合模块（AKSFFM）通过多尺度卷积操作和注意力权重学习机制增强特征融合。此外，我们提出的ARSOD-YOLO基于YOLOv8对网络架构、组件模块和损失函数进行了优化，在保持模型效率的同时增强了出色的小目标检测能力。我们在VEDAI和AI-TOD数据集上进行了实验，展示了ARSOD-YOLO的优异性能。我们的算法在VEDAI数据集上的mAP50达到了74.3%，比YOLOv8基线高出3.1%。同样，在AI-TOD数据集上，mAP50达到了47.8%，超过基线网络6.1%。
