稳健性(进化)
计算机科学
算法
目标检测
特征提取
拍打
人工智能
计算机视觉
模式识别(心理学)
翼
工程类
航空航天工程
生物化学
化学
基因
作者
Chao Ma,Kun Du,Askar Hamdulla
摘要
This paper proposes an algorithm based on an improved version of YOLOv8l, which is designed for small target detection on flapping wing drones. By adding a small object detection layer and introducing Multi-head self-attention (MHSA), the algorithm effectively reduces interference from irrelevant backgrounds and enhances the network's feature extraction performance. Experimental results on both the flapping wing dataset and the VisDrone dataset demonstrate that compared with the baseline YOLOv8l algorithm, the improved algorithm shows a 4.1% and 3.3% improvement in the mAP@.5 and mAP@.5:.95 indicators, respectively, and a 5.9% and 4% improvement on the VisDrone dataset. Particularly noteworthy is the improved algorithm's performance on the mAP@.5 index, which achieved 50.1% on the VisDrone dataset, proving its robustness and exceptional performance in small target detection. These results illustrate the algorithm's effectiveness and practicality, making it a valuable tool for flapping wing UAV vision applications.
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