Liao Huan, Zhu Wenqiu
School of Computer Science, Hunan University of Technology, Zhuzhou 412007, China.
Biomimetics (Basel). 2023 Oct 1;8(6):458. doi: 10.3390/biomimetics8060458.
Bioinspired object detection in remotely sensed images plays an important role in a variety of fields. Due to the small size of the target, complex background information, and multi-scale remote sensing images, the generalized YOLOv5 detection framework is unable to obtain good detection results. In order to deal with this issue, we proposed YOLO-DRS, a bioinspired object detection algorithm for remote sensing images incorporating a multi-scale efficient lightweight attention mechanism. First, we proposed LEC, a lightweight multi-scale module for efficient attention mechanisms. The fusion of multi-scale feature information allows the LEC module to completely improve the model's ability to extract multi-scale targets and recognize more targets. Then, we propose a transposed convolutional upsampling alternative to the original nearest-neighbor interpolation algorithm. Transposed convolutional upsampling has the potential to greatly reduce the loss of feature information by learning the feature information dynamically, thereby reducing problems such as missed detections and false detections of small targets by the model. Our proposed YOLO-DRS algorithm exhibits significant improvements over the original YOLOv5s. Specifically, it achieves a 2.3% increase in precision (P), a 3.2% increase in recall (R), and a 2.5% increase in mAP@0.5. Notably, the introduction of the LEC module and transposed convolutional results in a respective improvement of 2.2% and 2.1% in mAP@0.5. In addition, YOLO-DRS only increased the GFLOPs by 0.2. In comparison to the state-of-the-art algorithms, namely YOLOv8s and YOLOv7-tiny, YOLO-DRS demonstrates significant improvements in the mAP@0.5 metrics, with enhancements ranging from 1.8% to 7.3%. It is fully proved that our YOLO-DRS can reduce the missed and false detection problems of remote sensing target detection.
遥感图像中的仿生目标检测在多个领域发挥着重要作用。由于目标尺寸小、背景信息复杂以及遥感图像多尺度等特点，通用的YOLOv5检测框架无法获得良好的检测结果。为解决这一问题，我们提出了YOLO-DRS，一种用于遥感图像的仿生目标检测算法，它融入了多尺度高效轻量级注意力机制。首先，我们提出了LEC，一种用于高效注意力机制的轻量级多尺度模块。多尺度特征信息的融合使LEC模块能够全面提升模型提取多尺度目标和识别更多目标的能力。然后，我们提出了一种转置卷积上采样方法来替代原始的最近邻插值算法。转置卷积上采样有潜力通过动态学习特征信息大幅减少特征信息损失，从而减少模型对小目标的漏检和误检等问题。我们提出的YOLO-DRS算法相较于原始的YOLOv5s有显著提升。具体而言，其精度（P）提高了2.3%，召回率（R）提高了3.2%，mAP@0.5提高了2.5%。值得注意的是，LEC模块和转置卷积的引入分别使mAP@0.5提升了2.2%和2.1%。此外，YOLO-DRS仅使GFLOPs增加了0.2。与当前最先进的算法YOLOv8s和YOLOv7-tiny相比，YOLO-DRS在mAP@0.5指标上有显著提升，提升幅度在1.8%至7.3%之间。充分证明了我们的YOLO-DRS能够减少遥感目标检测中的漏检和误检问题。
