Fang Ao, Feng Song, Liang Bo, Jiang Ji
Yunnan Key Laboratory of Computer Technology Application, Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.
Yunnan Police College, Kunming 650223, China.
Sensors (Basel). 2024 Jun 17;24(12):3915. doi: 10.3390/s24123915.
Aiming at real-time detection of UAVs, small UAV targets are easily missed and difficult to detect in complex backgrounds. To maintain high detection performance while reducing memory and computational costs, this paper proposes the SEB-YOLOv8s detection method. Firstly, the YOLOv8 network structure is reconstructed using SPD-Conv to reduce the computational burden and accelerate the processing speed while retaining more shallow features of small targets. Secondly, we design the AttC2f module and replace the C2f module in the backbone of YOLOv8s with it, enhancing the model's ability to obtain accurate information and enriching the extracted relevant information. Finally, Bi-Level Routing Attention is introduced to optimize the Neck part of the network, reducing the model's attention to interfering information and filtering it out. The experimental results show that the mAP50 of the proposed method reaches 90.5% and the accuracy reaches 95.9%, which are improvements of 2.2% and 1.9%, respectively, compared with the original model. The mAP50-95 is improved by 2.7%, and the model's occupied memory size only increases by 2.5 MB, effectively achieving high-accuracy real-time detection with low memory consumption.
针对无人机的实时检测，在复杂背景下，小型无人机目标容易被遗漏且难以检测。为了在降低内存和计算成本的同时保持高检测性能，本文提出了SEB-YOLOv8s检测方法。首先，使用SPD-Conv对YOLOv8网络结构进行重构，在保留小目标更多浅层特征的同时减轻计算负担并加快处理速度。其次，设计AttC2f模块并将其替换YOLOv8s主干中的C2f模块，增强模型获取准确信息的能力并丰富提取的相关信息。最后，引入双层路由注意力来优化网络的颈部部分，减少模型对干扰信息的关注并将其过滤掉。实验结果表明，该方法的mAP50达到90.5%，准确率达到95.9%，与原始模型相比分别提高了2.2%和1.9%。mAP50-95提高了2.7%，且模型占用的内存大小仅增加了2.5 MB，有效实现了低内存消耗下的高精度实时检测。
