计算机工程与应用2025，Vol.61Issue(2)：97-111,15.DOI:10.3778/j.issn.1002-8331.2403-0007
多任务联合学习下的复杂天气航拍图像目标检测算法
Object Detection Algorithm of Aerial Image in Complex Weather Based on Multi-Task Joint Learning
摘要
Abstract
Aiming at the problem of poor object detection effect caused by the degradation of UAV image quality in com-plex weather such as rain and fog,a target detection algorithm CGP-YOLO(context-guided and prompt-based YOLOv8)based on context guidance and prompt learning is proposed.A multi-task joint learning detection network is constructed to balance detection and image restoration tasks through a two-branch structure.A cross-layer attention-weighted denoising branch based on prompt learning is proposed to guide the network to reconstruct clear images using degradation prompts.The model backbone is designed with a context-based residual sampling module,and the convolutional attention mecha-nism is integrated into it,so that the local and global information of the target can be integrated.The separable large-core multi-scale feature extraction module is used to process the multi-scale features of the network.The special detection head of small object is introduced to enhance the detection accuracy of small object.The experimental results show that with only 60%of the parameters of the baseline model,the detection accuracy of the model gets an increase of 2.4 percentage points,and the average accuracy(mAP)is increased by 2.04 percentage points.The model detection effect is better than other classical models and has excellent performance.关键词
多任务学习/目标检测/无人机图像/复杂天气/提示学习/去噪模型Key words
multi-task learning/object detection/UAV image/complex weather/prompt learning/denoising model分类
信息技术与安全科学引用本文复制引用
王新蕾,王硕,翟嘉政,肖瑞林,廖晨旭..多任务联合学习下的复杂天气航拍图像目标检测算法[J].计算机工程与应用,2025,61(2):97-111,15.基金项目
国家重点研发计划科技创新·2030-"新一代人工智能"重大项目(2021ZD0112200). （2021ZD0112200）
